categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
cs.AI cs.LG | null | 1101.2320 | null | null | http://arxiv.org/pdf/1101.2320v1 | 2011-01-12T10:49:51Z | 2011-01-12T10:49:51Z | Review and Evaluation of Feature Selection Algorithms in Synthetic
Problems | The main purpose of Feature Subset Selection is to find a reduced subset of
attributes from a data set described by a feature set. The task of a feature
selection algorithm (FSA) is to provide with a computational solution motivated
by a certain definition of relevance or by a reliable evaluation measure. In
this paper several fundamental algorithms are studied to assess their
performance in a controlled experimental scenario. A measure to evaluate FSAs
is devised that computes the degree of matching between the output given by a
FSA and the known optimal solutions. An extensive experimental study on
synthetic problems is carried out to assess the behaviour of the algorithms in
terms of solution accuracy and size as a function of the relevance,
irrelevance, redundancy and size of the data samples. The controlled
experimental conditions facilitate the derivation of better-supported and
meaningful conclusions.
| [
"['L. A. Belanche' 'F. F. González']",
"L.A. Belanche and F.F. Gonz\\'alez"
] |
cs.CV cs.LG | null | 1101.2987 | null | null | http://arxiv.org/pdf/1101.2987v1 | 2011-01-15T13:29:12Z | 2011-01-15T13:29:12Z | Support vector machines/relevance vector machine for remote sensing
classification: A review | Kernel-based machine learning algorithms are based on mapping data from the
original input feature space to a kernel feature space of higher dimensionality
to solve a linear problem in that space. Over the last decade, kernel based
classification and regression approaches such as support vector machines have
widely been used in remote sensing as well as in various civil engineering
applications. In spite of their better performance with different datasets,
support vector machines still suffer from shortcomings such as
visualization/interpretation of model, choice of kernel and kernel specific
parameter as well as the regularization parameter. Relevance vector machines
are another kernel based approach being explored for classification and
regression with in last few years. The advantages of the relevance vector
machines over the support vector machines is the availability of probabilistic
predictions, using arbitrary kernel functions and not requiring setting of the
regularization parameter. This paper presents a state-of-the-art review of SVM
and RVM in remote sensing and provides some details of their use in other civil
engineering application also.
| [
"['Mahesh Pal']",
"Mahesh Pal"
] |
stat.ME cs.LG stat.ML | null | 1101.3594 | null | null | http://arxiv.org/pdf/1101.3594v2 | 2012-01-05T18:04:10Z | 2011-01-19T00:41:43Z | Classification under Data Contamination with Application to Remote
Sensing Image Mis-registration | This work is motivated by the problem of image mis-registration in remote
sensing and we are interested in determining the resulting loss in the accuracy
of pattern classification. A statistical formulation is given where we propose
to use data contamination to model and understand the phenomenon of image
mis-registration. This model is widely applicable to many other types of errors
as well, for example, measurement errors and gross errors etc. The impact of
data contamination on classification is studied under a statistical learning
theoretical framework. A closed-form asymptotic bound is established for the
resulting loss in classification accuracy, which is less than
$\epsilon/(1-\epsilon)$ for data contamination of an amount of $\epsilon$. Our
bound is sharper than similar bounds in the domain adaptation literature and,
unlike such bounds, it applies to classifiers with an infinite
Vapnik-Chervonekis (VC) dimension. Extensive simulations have been conducted on
both synthetic and real datasets under various types of data contamination,
including label flipping, feature swapping and the replacement of feature
values with data generated from a random source such as a Gaussian or Cauchy
distribution. Our simulation results show that the bound we derive is fairly
tight.
| [
"Donghui Yan, Peng Gong, Aiyou Chen and Liheng Zhong",
"['Donghui Yan' 'Peng Gong' 'Aiyou Chen' 'Liheng Zhong']"
] |
cs.AI cs.LG cs.SY math.OC | null | 1101.4003 | null | null | http://arxiv.org/pdf/1101.4003v3 | 2011-07-30T09:56:22Z | 2011-01-20T19:51:58Z | Dyna-H: a heuristic planning reinforcement learning algorithm applied to
role-playing-game strategy decision systems | In a Role-Playing Game, finding optimal trajectories is one of the most
important tasks. In fact, the strategy decision system becomes a key component
of a game engine. Determining the way in which decisions are taken (online,
batch or simulated) and the consumed resources in decision making (e.g.
execution time, memory) will influence, in mayor degree, the game performance.
When classical search algorithms such as A* can be used, they are the very
first option. Nevertheless, such methods rely on precise and complete models of
the search space, and there are many interesting scenarios where their
application is not possible. Then, model free methods for sequential decision
making under uncertainty are the best choice. In this paper, we propose a
heuristic planning strategy to incorporate the ability of heuristic-search in
path-finding into a Dyna agent. The proposed Dyna-H algorithm, as A* does,
selects branches more likely to produce outcomes than other branches. Besides,
it has the advantages of being a model-free online reinforcement learning
algorithm. The proposal was evaluated against the one-step Q-Learning and
Dyna-Q algorithms obtaining excellent experimental results: Dyna-H
significantly overcomes both methods in all experiments. We suggest also, a
functional analogy between the proposed sampling from worst trajectories
heuristic and the role of dreams (e.g. nightmares) in human behavior.
| [
"['Matilde Santos' 'Jose Antonio Martin H.' 'Victoria Lopez'\n 'Guillermo Botella']",
"Matilde Santos, Jose Antonio Martin H., Victoria Lopez and Guillermo\n Botella"
] |
cs.LG | null | 1101.4170 | null | null | http://arxiv.org/pdf/1101.4170v1 | 2011-01-21T16:11:05Z | 2011-01-21T16:11:05Z | The Role of Normalization in the Belief Propagation Algorithm | An important part of problems in statistical physics and computer science can
be expressed as the computation of marginal probabilities over a Markov Random
Field. The belief propagation algorithm, which is an exact procedure to compute
these marginals when the underlying graph is a tree, has gained its popularity
as an efficient way to approximate them in the more general case. In this
paper, we focus on an aspect of the algorithm that did not get that much
attention in the literature, which is the effect of the normalization of the
messages. We show in particular that, for a large class of normalization
strategies, it is possible to focus only on belief convergence. Following this,
we express the necessary and sufficient conditions for local stability of a
fixed point in terms of the graph structure and the beliefs values at the fixed
point. We also explicit some connexion between the normalization constants and
the underlying Bethe Free Energy.
| [
"['Victorin Martin' 'Jean-Marc Lasgouttes' 'Cyril Furtlehner']",
"Victorin Martin and Jean-Marc Lasgouttes and Cyril Furtlehner"
] |
physics.data-an cond-mat.dis-nn cond-mat.stat-mech cs.LG | 10.1088/1742-5468/2011/08/P08009 | 1101.4227 | null | null | http://arxiv.org/abs/1101.4227v3 | 2011-10-31T03:46:11Z | 2011-01-21T20:37:31Z | Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs | We theoretically study semi-supervised clustering in sparse graphs in the
presence of pairwise constraints on the cluster assignments of nodes. We focus
on bi-cluster graphs, and study the impact of semi-supervision for varying
constraint density and overlap between the clusters. Recent results for
unsupervised clustering in sparse graphs indicate that there is a critical
ratio of within-cluster and between-cluster connectivities below which clusters
cannot be recovered with better than random accuracy. The goal of this paper is
to examine the impact of pairwise constraints on the clustering accuracy. Our
results suggests that the addition of constraints does not provide automatic
improvement over the unsupervised case. When the density of the constraints is
sufficiently small, their only impact is to shift the detection threshold while
preserving the criticality. Conversely, if the density of (hard) constraints is
above the percolation threshold, the criticality is suppressed and the
detection threshold disappears.
| [
"['Greg Ver Steeg' 'Aram Galstyan' 'Armen E. Allahverdyan']",
"Greg Ver Steeg, Aram Galstyan, Armen E. Allahverdyan"
] |
stat.ML cs.LG math.FA | 10.1016/j.acha.2012.03.009 | 1101.4388 | null | null | http://arxiv.org/abs/1101.4388v3 | 2012-03-28T18:46:47Z | 2011-01-23T16:57:03Z | Reproducing Kernel Banach Spaces with the l1 Norm | Targeting at sparse learning, we construct Banach spaces B of functions on an
input space X with the properties that (1) B possesses an l1 norm in the sense
that it is isometrically isomorphic to the Banach space of integrable functions
on X with respect to the counting measure; (2) point evaluations are continuous
linear functionals on B and are representable through a bilinear form with a
kernel function; (3) regularized learning schemes on B satisfy the linear
representer theorem. Examples of kernel functions admissible for the
construction of such spaces are given.
| [
"['Guohui Song' 'Haizhang Zhang' 'Fred J. Hickernell']",
"Guohui Song, Haizhang Zhang, Fred J. Hickernell"
] |
stat.ML cs.LG math.FA | null | 1101.4439 | null | null | http://arxiv.org/pdf/1101.4439v2 | 2011-01-27T14:45:29Z | 2011-01-24T03:39:57Z | Reproducing Kernel Banach Spaces with the l1 Norm II: Error Analysis for
Regularized Least Square Regression | A typical approach in estimating the learning rate of a regularized learning
scheme is to bound the approximation error by the sum of the sampling error,
the hypothesis error and the regularization error. Using a reproducing kernel
space that satisfies the linear representer theorem brings the advantage of
discarding the hypothesis error from the sum automatically. Following this
direction, we illustrate how reproducing kernel Banach spaces with the l1 norm
can be applied to improve the learning rate estimate of l1-regularization in
machine learning.
| [
"['Guohui Song' 'Haizhang Zhang']",
"Guohui Song, Haizhang Zhang"
] |
cs.LG | null | 1101.4681 | null | null | http://arxiv.org/pdf/1101.4681v6 | 2013-06-27T00:48:11Z | 2011-01-24T22:12:37Z | Close the Gaps: A Learning-while-Doing Algorithm for a Class of
Single-Product Revenue Management Problems | We consider a retailer selling a single product with limited on-hand
inventory over a finite selling season. Customer demand arrives according to a
Poisson process, the rate of which is influenced by a single action taken by
the retailer (such as price adjustment, sales commission, advertisement
intensity, etc.). The relationship between the action and the demand rate is
not known in advance. However, the retailer is able to learn the optimal action
"on the fly" as she maximizes her total expected revenue based on the observed
demand reactions.
Using the pricing problem as an example, we propose a dynamic
"learning-while-doing" algorithm that only involves function value estimation
to achieve a near-optimal performance. Our algorithm employs a series of
shrinking price intervals and iteratively tests prices within that interval
using a set of carefully chosen parameters. We prove that the convergence rate
of our algorithm is among the fastest of all possible algorithms in terms of
asymptotic "regret" (the relative loss comparing to the full information
optimal solution). Our result closes the performance gaps between parametric
and non-parametric learning and between a post-price mechanism and a
customer-bidding mechanism. Important managerial insight from this research is
that the values of information on both the parametric form of the demand
function as well as each customer's exact reservation price are less important
than prior literature suggests. Our results also suggest that firms would be
better off to perform dynamic learning and action concurrently rather than
sequentially.
| [
"['Zizhuo Wang' 'Shiming Deng' 'Yinyu Ye']",
"Zizhuo Wang, Shiming Deng and Yinyu Ye"
] |
cs.CV cs.LG | 10.1117/1.3595426 | 1101.4749 | null | null | http://arxiv.org/abs/1101.4749v1 | 2011-01-25T09:11:49Z | 2011-01-25T09:11:49Z | Online Adaptive Decision Fusion Framework Based on Entropic Projections
onto Convex Sets with Application to Wildfire Detection in Video | In this paper, an Entropy functional based online Adaptive Decision Fusion
(EADF) framework is developed for image analysis and computer vision
applications. In this framework, it is assumed that the compound algorithm
consists of several sub-algorithms each of which yielding its own decision as a
real number centered around zero, representing the confidence level of that
particular sub-algorithm. Decision values are linearly combined with weights
which are updated online according to an active fusion method based on
performing entropic projections onto convex sets describing sub-algorithms. It
is assumed that there is an oracle, who is usually a human operator, providing
feedback to the decision fusion method. A video based wildfire detection system
is developed to evaluate the performance of the algorithm in handling the
problems where data arrives sequentially. In this case, the oracle is the
security guard of the forest lookout tower verifying the decision of the
combined algorithm. Simulation results are presented. The EADF framework is
also tested with a standard dataset.
| [
"Osman Gunay and Behcet Ugur Toreyin and Kivanc Kose and A. Enis Cetin",
"['Osman Gunay' 'Behcet Ugur Toreyin' 'Kivanc Kose' 'A. Enis Cetin']"
] |
cs.LG math.OC | null | 1101.4752 | null | null | http://arxiv.org/pdf/1101.4752v3 | 2012-04-02T22:59:53Z | 2011-01-25T09:18:46Z | A Primal-Dual Convergence Analysis of Boosting | Boosting combines weak learners into a predictor with low empirical risk. Its
dual constructs a high entropy distribution upon which weak learners and
training labels are uncorrelated. This manuscript studies this primal-dual
relationship under a broad family of losses, including the exponential loss of
AdaBoost and the logistic loss, revealing:
- Weak learnability aids the whole loss family: for any {\epsilon}>0,
O(ln(1/{\epsilon})) iterations suffice to produce a predictor with empirical
risk {\epsilon}-close to the infimum;
- The circumstances granting the existence of an empirical risk minimizer may
be characterized in terms of the primal and dual problems, yielding a new proof
of the known rate O(ln(1/{\epsilon}));
- Arbitrary instances may be decomposed into the above two, granting rate
O(1/{\epsilon}), with a matching lower bound provided for the logistic loss.
| [
"['Matus Telgarsky']",
"Matus Telgarsky"
] |
cs.LG cs.AI cs.CV | null | 1101.4918 | null | null | http://arxiv.org/pdf/1101.4918v1 | 2011-01-25T20:24:25Z | 2011-01-25T20:24:25Z | Using Feature Weights to Improve Performance of Neural Networks | Different features have different relevance to a particular learning problem.
Some features are less relevant; while some very important. Instead of
selecting the most relevant features using feature selection, an algorithm can
be given this knowledge of feature importance based on expert opinion or prior
learning. Learning can be faster and more accurate if learners take feature
importance into account. Correlation aided Neural Networks (CANN) is presented
which is such an algorithm. CANN treats feature importance as the correlation
coefficient between the target attribute and the features. CANN modifies normal
feed-forward Neural Network to fit both correlation values and training data.
Empirical evaluation shows that CANN is faster and more accurate than applying
the two step approach of feature selection and then using normal learning
algorithms.
| [
"['Ridwan Al Iqbal']",
"Ridwan Al Iqbal"
] |
cs.LG cs.AI cs.CV | null | 1101.4924 | null | null | http://arxiv.org/pdf/1101.4924v1 | 2011-01-25T20:42:01Z | 2011-01-25T20:42:01Z | A Generalized Method for Integrating Rule-based Knowledge into Inductive
Methods Through Virtual Sample Creation | Hybrid learning methods use theoretical knowledge of a domain and a set of
classified examples to develop a method for classification. Methods that use
domain knowledge have been shown to perform better than inductive learners.
However, there is no general method to include domain knowledge into all
inductive learning algorithms as all hybrid methods are highly specialized for
a particular algorithm. We present an algorithm that will take domain knowledge
in the form of propositional rules, generate artificial examples from the rules
and also remove instances likely to be flawed. This enriched dataset then can
be used by any learning algorithm. Experimental results of different scenarios
are shown that demonstrate this method to be more effective than simple
inductive learning.
| [
"['Ridwan Al Iqbal']",
"Ridwan Al Iqbal"
] |
cs.LG | null | 1101.5039 | null | null | http://arxiv.org/pdf/1101.5039v1 | 2011-01-26T12:21:13Z | 2011-01-26T12:21:13Z | A Novel Template-Based Learning Model | This article presents a model which is capable of learning and abstracting
new concepts based on comparing observations and finding the resemblance
between the observations. In the model, the new observations are compared with
the templates which have been derived from the previous experiences. In the
first stage, the objects are first represented through a geometric description
which is used for finding the object boundaries and a descriptor which is
inspired by the human visual system and then they are fed into the model. Next,
the new observations are identified through comparing them with the
previously-learned templates and are used for producing new templates. The
comparisons are made based on measures like Euclidean or correlation distance.
The new template is created by applying onion-pealing algorithm. The algorithm
consecutively uses convex hulls which are made by the points representing the
objects. If the new observation is remarkably similar to one of the observed
categories, it is no longer utilized in creating a new template. The existing
templates are used to provide a description of the new observation. This
description is provided in the templates space. Each template represents a
dimension of the feature space. The degree of the resemblance each template
bears to each object indicates the value associated with the object in that
dimension of the templates space. In this way, the description of the new
observation becomes more accurate and detailed as the time passes and the
experiences increase. We have used this model for learning and recognizing the
new polygons in the polygon space. Representing the polygons was made possible
through employing a geometric method and a method inspired by human visual
system. Various implementations of the model have been compared. The evaluation
results of the model prove its efficiency in learning and deriving new
templates.
| [
"Mohammadreza Abolghasemi-Dahaghani, Farzad Didehvar (1), Alireza\n Nowroozi",
"['Mohammadreza Abolghasemi-Dahaghani' 'Farzad Didehvar' 'Alireza Nowroozi']"
] |
cs.SI cs.LG physics.soc-ph | null | 1101.5097 | null | null | http://arxiv.org/pdf/1101.5097v1 | 2011-01-26T16:15:22Z | 2011-01-26T16:15:22Z | Infinite Multiple Membership Relational Modeling for Complex Networks | Learning latent structure in complex networks has become an important problem
fueled by many types of networked data originating from practically all fields
of science. In this paper, we propose a new non-parametric Bayesian
multiple-membership latent feature model for networks. Contrary to existing
multiple-membership models that scale quadratically in the number of vertices
the proposed model scales linearly in the number of links admitting
multiple-membership analysis in large scale networks. We demonstrate a
connection between the single membership relational model and multiple
membership models and show on "real" size benchmark network data that
accounting for multiple memberships improves the learning of latent structure
as measured by link prediction while explicitly accounting for multiple
membership result in a more compact representation of the latent structure of
networks.
| [
"['Morten Mørup' 'Mikkel N. Schmidt' 'Lars Kai Hansen']",
"Morten M{\\o}rup, Mikkel N. Schmidt, Lars Kai Hansen"
] |
physics.data-an cs.LG cs.SI physics.soc-ph | null | 1101.5141 | null | null | http://arxiv.org/pdf/1101.5141v1 | 2011-01-26T19:58:58Z | 2011-01-26T19:58:58Z | A Complex Networks Approach for Data Clustering | Many methods have been developed for data clustering, such as k-means,
expectation maximization and algorithms based on graph theory. In this latter
case, graphs are generally constructed by taking into account the Euclidian
distance as a similarity measure, and partitioned using spectral methods.
However, these methods are not accurate when the clusters are not well
separated. In addition, it is not possible to automatically determine the
number of clusters. These limitations can be overcome by taking into account
network community identification algorithms. In this work, we propose a
methodology for data clustering based on complex networks theory. We compare
different metrics for quantifying the similarity between objects and take into
account three community finding techniques. This approach is applied to two
real-world databases and to two sets of artificially generated data. By
comparing our method with traditional clustering approaches, we verify that the
proximity measures given by the Chebyshev and Manhattan distances are the most
suitable metrics to quantify the similarity between objects. In addition, the
community identification method based on the greedy optimization provides the
smallest misclassification rates.
| [
"['Francisco A. Rodrigues' 'Guilherme Ferraz de Arruda'\n 'Luciano da Fontoura Costa']",
"Francisco A. Rodrigues, Guilherme Ferraz de Arruda, Luciano da\n Fontoura Costa"
] |
cs.LG cs.AI cs.MA cs.RO | null | 1101.5632 | null | null | http://arxiv.org/pdf/1101.5632v1 | 2011-01-28T21:27:31Z | 2011-01-28T21:27:31Z | Active Markov Information-Theoretic Path Planning for Robotic
Environmental Sensing | Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequently scale poorly with the length of history of
observations. Hence, it becomes computationally impractical to use these
strategies for in situ, real-time active sampling. To ease this computational
burden, this paper presents a Markov-based approach to efficient
information-theoretic path planning for active sampling of GP-based fields. We
analyze the time complexity of solving the Markov-based path planning problem,
and demonstrate analytically that it scales better than that of deriving the
non-Markovian strategies with increasing length of planning horizon. For a
class of exploration tasks called the transect sampling task, we provide
theoretical guarantees on the active sampling performance of our Markov-based
policy, from which ideal environmental field conditions and sampling task
settings can be established to limit its performance degradation due to
violation of the Markov assumption. Empirical evaluation on real-world
temperature and plankton density field data shows that our Markov-based policy
can generally achieve active sampling performance comparable to that of the
widely-used non-Markovian greedy policies under less favorable realistic field
conditions and task settings while enjoying significant computational gain over
them.
| [
"Kian Hsiang Low, John M. Dolan, and Pradeep Khosla",
"['Kian Hsiang Low' 'John M. Dolan' 'Pradeep Khosla']"
] |
cs.IT cs.LG math.IT | null | 1101.5672 | null | null | http://arxiv.org/pdf/1101.5672v1 | 2011-01-29T06:06:56Z | 2011-01-29T06:06:56Z | On the Local Correctness of L^1 Minimization for Dictionary Learning | The idea that many important classes of signals can be well-represented by
linear combinations of a small set of atoms selected from a given dictionary
has had dramatic impact on the theory and practice of signal processing. For
practical problems in which an appropriate sparsifying dictionary is not known
ahead of time, a very popular and successful heuristic is to search for a
dictionary that minimizes an appropriate sparsity surrogate over a given set of
sample data. While this idea is appealing, the behavior of these algorithms is
largely a mystery; although there is a body of empirical evidence suggesting
they do learn very effective representations, there is little theory to
guarantee when they will behave correctly, or when the learned dictionary can
be expected to generalize. In this paper, we take a step towards such a theory.
We show that under mild hypotheses, the dictionary learning problem is locally
well-posed: the desired solution is indeed a local minimum of the $\ell^1$
norm. Namely, if $\mb A \in \Re^{m \times n}$ is an incoherent (and possibly
overcomplete) dictionary, and the coefficients $\mb X \in \Re^{n \times p}$
follow a random sparse model, then with high probability $(\mb A,\mb X)$ is a
local minimum of the $\ell^1$ norm over the manifold of factorizations $(\mb
A',\mb X')$ satisfying $\mb A' \mb X' = \mb Y$, provided the number of samples
$p = \Omega(n^3 k)$. For overcomplete $\mb A$, this is the first result showing
that the dictionary learning problem is locally solvable. Our analysis draws on
tools developed for the problem of completing a low-rank matrix from a small
subset of its entries, which allow us to overcome a number of technical
obstacles; in particular, the absence of the restricted isometry property.
| [
"Quan Geng and Huan Wang and John Wright",
"['Quan Geng' 'Huan Wang' 'John Wright']"
] |
cs.CV cs.LG | 10.1109/TSP.2011.2168521 | 1101.5785 | null | null | http://arxiv.org/abs/1101.5785v1 | 2011-01-30T17:16:55Z | 2011-01-30T17:16:55Z | Statistical Compressed Sensing of Gaussian Mixture Models | A novel framework of compressed sensing, namely statistical compressed
sensing (SCS), that aims at efficiently sampling a collection of signals that
follow a statistical distribution, and achieving accurate reconstruction on
average, is introduced. SCS based on Gaussian models is investigated in depth.
For signals that follow a single Gaussian model, with Gaussian or Bernoulli
sensing matrices of O(k) measurements, considerably smaller than the O(k
log(N/k)) required by conventional CS based on sparse models, where N is the
signal dimension, and with an optimal decoder implemented via linear filtering,
significantly faster than the pursuit decoders applied in conventional CS, the
error of SCS is shown tightly upper bounded by a constant times the best k-term
approximation error, with overwhelming probability. The failure probability is
also significantly smaller than that of conventional sparsity-oriented CS.
Stronger yet simpler results further show that for any sensing matrix, the
error of Gaussian SCS is upper bounded by a constant times the best k-term
approximation with probability one, and the bound constant can be efficiently
calculated. For Gaussian mixture models (GMMs), that assume multiple Gaussian
distributions and that each signal follows one of them with an unknown index, a
piecewise linear estimator is introduced to decode SCS. The accuracy of model
selection, at the heart of the piecewise linear decoder, is analyzed in terms
of the properties of the Gaussian distributions and the number of sensing
measurements. A maximum a posteriori expectation-maximization algorithm that
iteratively estimates the Gaussian models parameters, the signals model
selection, and decodes the signals, is presented for GMM-based SCS. In real
image sensing applications, GMM-based SCS is shown to lead to improved results
compared to conventional CS, at a considerably lower computational cost.
| [
"Guoshen Yu and Guillermo Sapiro",
"['Guoshen Yu' 'Guillermo Sapiro']"
] |
cs.DB cs.DS cs.LG | null | 1101.5805 | null | null | http://arxiv.org/pdf/1101.5805v3 | 2011-08-11T20:56:03Z | 2011-01-30T19:13:43Z | The VC-Dimension of Queries and Selectivity Estimation Through Sampling | We develop a novel method, based on the statistical concept of the
Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality)
of SQL queries - a crucial step in optimizing the execution of large scale
database and data-mining operations. The major theoretical contribution of this
work, which is of independent interest, is an explicit bound to the
VC-dimension of a range space defined by all possible outcomes of a collection
(class) of queries. We prove that the VC-dimension is a function of the maximum
number of Boolean operations in the selection predicate and of the maximum
number of select and join operations in any individual query in the collection,
but it is neither a function of the number of queries in the collection nor of
the size (number of tuples) of the database. We leverage on this result and
develop a method that, given a class of queries, builds a concise random sample
of a database, such that with high probability the execution of any query in
the class on the sample provides an accurate estimate for the selectivity of
the query on the original large database. The error probability holds
simultaneously for the selectivity estimates of all queries in the collection,
thus the same sample can be used to evaluate the selectivity of multiple
queries, and the sample needs to be refreshed only following major changes in
the database. The sample representation computed by our method is typically
sufficiently small to be stored in main memory. We present extensive
experimental results, validating our theoretical analysis and demonstrating the
advantage of our technique when compared to complex selectivity estimation
techniques used in PostgreSQL and the Microsoft SQL Server.
| [
"Matteo Riondato, Mert Akdere, Ugur Cetintemel, Stanley B. Zdonik, Eli\n Upfal",
"['Matteo Riondato' 'Mert Akdere' 'Ugur Cetintemel' 'Stanley B. Zdonik'\n 'Eli Upfal']"
] |
cs.LG cs.CG cs.DB | null | 1102.0026 | null | null | http://arxiv.org/pdf/1102.0026v1 | 2011-01-31T22:21:19Z | 2011-01-31T22:21:19Z | Spatially-Aware Comparison and Consensus for Clusterings | This paper proposes a new distance metric between clusterings that
incorporates information about the spatial distribution of points and clusters.
Our approach builds on the idea of a Hilbert space-based representation of
clusters as a combination of the representations of their constituent points.
We use this representation and the underlying metric to design a
spatially-aware consensus clustering procedure. This consensus procedure is
implemented via a novel reduction to Euclidean clustering, and is both simple
and efficient. All of our results apply to both soft and hard clusterings. We
accompany these algorithms with a detailed experimental evaluation that
demonstrates the efficiency and quality of our techniques.
| [
"Parasaran Raman, Jeff M. Phillips and Suresh Venkatasubramanian",
"['Parasaran Raman' 'Jeff M. Phillips' 'Suresh Venkatasubramanian']"
] |
stat.ME cs.CE cs.CV cs.LG q-bio.QM | 10.1214/12-AOAS543 | 1102.0059 | null | null | http://arxiv.org/abs/1102.0059v2 | 2012-10-01T09:20:39Z | 2011-02-01T02:08:00Z | Statistical methods for tissue array images - algorithmic scoring and
co-training | Recent advances in tissue microarray technology have allowed
immunohistochemistry to become a powerful medium-to-high throughput analysis
tool, particularly for the validation of diagnostic and prognostic biomarkers.
However, as study size grows, the manual evaluation of these assays becomes a
prohibitive limitation; it vastly reduces throughput and greatly increases
variability and expense. We propose an algorithm - Tissue Array Co-Occurrence
Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on
textural regularity summarized by local inter-pixel relationships. The
algorithm can be easily trained for any staining pattern, is absent of
sensitive tuning parameters and has the ability to report salient pixels in an
image that contribute to its score. Pathologists' input via informative
training patches is an important aspect of the algorithm that allows the
training for any specific marker or cell type. With co-training, the error rate
of TACOMA can be reduced substantially for a very small training sample (e.g.,
with size 30). We give theoretical insights into the success of co-training via
thinning of the feature set in a high-dimensional setting when there is
"sufficient" redundancy among the features. TACOMA is flexible, transparent and
provides a scoring process that can be evaluated with clarity and confidence.
In a study based on an estrogen receptor (ER) marker, we show that TACOMA is
comparable to, or outperforms, pathologists' performance in terms of accuracy
and repeatability.
| [
"Donghui Yan, Pei Wang, Michael Linden, Beatrice Knudsen, Timothy\n Randolph",
"['Donghui Yan' 'Pei Wang' 'Michael Linden' 'Beatrice Knudsen'\n 'Timothy Randolph']"
] |
cs.LG | null | 1102.0836 | null | null | http://arxiv.org/pdf/1102.0836v2 | 2011-02-08T04:03:50Z | 2011-02-04T04:40:07Z | EigenNet: A Bayesian hybrid of generative and conditional models for
sparse learning | It is a challenging task to select correlated variables in a high dimensional
space. To address this challenge, the elastic net has been developed and
successfully applied to many applications. Despite its great success, the
elastic net does not explicitly use correlation information embedded in data to
select correlated variables. To overcome this limitation, we present a novel
Bayesian hybrid model, the EigenNet, that uses the eigenstructures of data to
guide variable selection. Specifically, it integrates a sparse conditional
classification model with a generative model capturing variable correlations in
a principled Bayesian framework. We reparameterize the hybrid model in the
eigenspace to avoid overfiting and to increase the computational efficiency of
its MCMC sampler. Furthermore, we provide an alternative view to the EigenNet
from a regularization perspective: the EigenNet has an adaptive
eigenspace-based composite regularizer, which naturally generalizes the
$l_{1/2}$ regularizer used by the elastic net. Experiments on synthetic and
real data show that the EigenNet significantly outperforms the lasso, the
elastic net, and the Bayesian lasso in terms of prediction accuracy, especially
when the number of training samples is smaller than the number of variables.
| [
"Yuan Qi, Feng Yan",
"['Yuan Qi' 'Feng Yan']"
] |
cs.AI cs.CV cs.LG math.NA math.PR | 10.5121/ijaia.2011.2101 | 1102.0899 | null | null | http://arxiv.org/abs/1102.0899v1 | 2011-02-04T13:00:06Z | 2011-02-04T13:00:06Z | Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov
Model | The ability to predict the intentions of people based solely on their visual
actions is a skill only performed by humans and animals. The intelligence of
current computer algorithms has not reached this level of complexity, but there
are several research efforts that are working towards it. With the number of
classification algorithms available, it is hard to determine which algorithm
works best for a particular situation. In classification of visual human intent
data, Hidden Markov Models (HMM), and their variants, are leading candidates.
The inability of HMMs to provide a probability in the observation to
observation linkages is a big downfall in this classification technique. If a
person is visually identifying an action of another person, they monitor
patterns in the observations. By estimating the next observation, people have
the ability to summarize the actions, and thus determine, with pretty good
accuracy, the intention of the person performing the action. These visual cues
and linkages are important in creating intelligent algorithms for determining
human actions based on visual observations.
The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm
which provides observation to observation linkages. The following research
addresses the theory behind Evidence Feed Forward HMMs, provides mathematical
proofs of their learning of these parameters to optimize the likelihood of
observations with a Evidence Feed Forwards HMM, which is important in all
computational intelligence algorithm, and gives comparative examples with
standard HMMs in classification of both visual action data and measurement
data; thus providing a strong base for Evidence Feed Forward HMMs in
classification of many types of problems.
| [
"['Michael DelRose' 'Christian Wagner' 'Philip Frederick']",
"Michael DelRose, Christian Wagner, Philip Frederick"
] |
cs.IR cs.AI cs.LG nlin.AO q-bio.OT | 10.1007/s12065-011-0052-5 | 1102.1027 | null | null | http://arxiv.org/abs/1102.1027v1 | 2011-02-04T22:10:45Z | 2011-02-04T22:10:45Z | Collective Classification of Textual Documents by Guided
Self-Organization in T-Cell Cross-Regulation Dynamics | We present and study an agent-based model of T-Cell cross-regulation in the
adaptive immune system, which we apply to binary classification. Our method
expands an existing analytical model of T-cell cross-regulation (Carneiro et
al. in Immunol Rev 216(1):48-68, 2007) that was used to study the
self-organizing dynamics of a single population of T-Cells in interaction with
an idealized antigen presenting cell capable of presenting a single antigen.
With agent-based modeling we are able to study the self-organizing dynamics of
multiple populations of distinct T-cells which interact via antigen presenting
cells that present hundreds of distinct antigens. Moreover, we show that such
self-organizing dynamics can be guided to produce an effective binary
classification of antigens, which is competitive with existing machine learning
methods when applied to biomedical text classification. More specifically, here
we test our model on a dataset of publicly available full-text biomedical
articles provided by the BioCreative challenge (Krallinger in The biocreative
ii. 5 challenge overview, p 19, 2009). We study the robustness of our model's
parameter configurations, and show that it leads to encouraging results
comparable to state-of-the-art classifiers. Our results help us understand both
T-cell cross-regulation as a general principle of guided self-organization, as
well as its applicability to document classification. Therefore, we show that
our bio-inspired algorithm is a promising novel method for biomedical article
classification and for binary document classification in general.
| [
"Alaa Abi-Haidar and Luis M. Rocha",
"['Alaa Abi-Haidar' 'Luis M. Rocha']"
] |
cond-mat.stat-mech cs.LG cs.SI physics.soc-ph | 10.1103/PhysRevLett.107.065701 | 1102.1182 | null | null | http://arxiv.org/abs/1102.1182v1 | 2011-02-06T18:43:03Z | 2011-02-06T18:43:03Z | Phase transition in the detection of modules in sparse networks | We present an asymptotically exact analysis of the problem of detecting
communities in sparse random networks. Our results are also applicable to
detection of functional modules, partitions, and colorings in noisy planted
models. Using a cavity method analysis, we unveil a phase transition from a
region where the original group assignment is undetectable to one where
detection is possible. In some cases, the detectable region splits into an
algorithmically hard region and an easy one. Our approach naturally translates
into a practical algorithm for detecting modules in sparse networks, and
learning the parameters of the underlying model.
| [
"Aurelien Decelle, Florent Krzakala, Cristopher Moore and Lenka\n Zdeborov\\'a",
"['Aurelien Decelle' 'Florent Krzakala' 'Cristopher Moore'\n 'Lenka Zdeborová']"
] |
cs.LG math.FA | null | 1102.1324 | null | null | http://arxiv.org/pdf/1102.1324v1 | 2011-02-07T14:41:30Z | 2011-02-07T14:41:30Z | Refinement of Operator-valued Reproducing Kernels | This paper studies the construction of a refinement kernel for a given
operator-valued reproducing kernel such that the vector-valued reproducing
kernel Hilbert space of the refinement kernel contains that of the given one as
a subspace. The study is motivated from the need of updating the current
operator-valued reproducing kernel in multi-task learning when underfitting or
overfitting occurs. Numerical simulations confirm that the established
refinement kernel method is able to meet this need. Various characterizations
are provided based on feature maps and vector-valued integral representations
of operator-valued reproducing kernels. Concrete examples of refining
translation invariant and finite Hilbert-Schmidt operator-valued reproducing
kernels are provided. Other examples include refinement of Hessian of
scalar-valued translation-invariant kernels and transformation kernels.
Existence and properties of operator-valued reproducing kernels preserved
during the refinement process are also investigated.
| [
"Yuesheng Xu, Haizhang Zhang, Qinghui Zhang",
"['Yuesheng Xu' 'Haizhang Zhang' 'Qinghui Zhang']"
] |
stat.ML cs.LG math.ST stat.TH | null | 1102.1465 | null | null | http://arxiv.org/pdf/1102.1465v6 | 2012-07-09T19:24:19Z | 2011-02-07T23:25:47Z | An Introduction to Artificial Prediction Markets for Classification | Prediction markets are used in real life to predict outcomes of interest such
as presidential elections. This paper presents a mathematical theory of
artificial prediction markets for supervised learning of conditional
probability estimators. The artificial prediction market is a novel method for
fusing the prediction information of features or trained classifiers, where the
fusion result is the contract price on the possible outcomes. The market can be
trained online by updating the participants' budgets using training examples.
Inspired by the real prediction markets, the equations that govern the market
are derived from simple and reasonable assumptions. Efficient numerical
algorithms are presented for solving these equations. The obtained artificial
prediction market is shown to be a maximum likelihood estimator. It generalizes
linear aggregation, existent in boosting and random forest, as well as logistic
regression and some kernel methods. Furthermore, the market mechanism allows
the aggregation of specialized classifiers that participate only on specific
instances. Experimental comparisons show that the artificial prediction markets
often outperform random forest and implicit online learning on synthetic data
and real UCI datasets. Moreover, an extensive evaluation for pelvic and
abdominal lymph node detection in CT data shows that the prediction market
improves adaboost's detection rate from 79.6% to 81.2% at 3 false
positives/volume.
| [
"['Adrian Barbu' 'Nathan Lay']",
"Adrian Barbu, Nathan Lay"
] |
cs.AI cs.LG | null | 1102.1808 | null | null | http://arxiv.org/pdf/1102.1808v3 | 2011-02-11T05:10:57Z | 2011-02-09T08:25:36Z | From Machine Learning to Machine Reasoning | A plausible definition of "reasoning" could be "algebraically manipulating
previously acquired knowledge in order to answer a new question". This
definition covers first-order logical inference or probabilistic inference. It
also includes much simpler manipulations commonly used to build large learning
systems. For instance, we can build an optical character recognition system by
first training a character segmenter, an isolated character recognizer, and a
language model, using appropriate labeled training sets. Adequately
concatenating these modules and fine tuning the resulting system can be viewed
as an algebraic operation in a space of models. The resulting model answers a
new question, that is, converting the image of a text page into a computer
readable text.
This observation suggests a conceptual continuity between algebraically rich
inference systems, such as logical or probabilistic inference, and simple
manipulations, such as the mere concatenation of trainable learning systems.
Therefore, instead of trying to bridge the gap between machine learning systems
and sophisticated "all-purpose" inference mechanisms, we can instead
algebraically enrich the set of manipulations applicable to training systems,
and build reasoning capabilities from the ground up.
| [
"Leon Bottou",
"['Leon Bottou']"
] |
cs.LG cs.IT math.IT | null | 1102.2467 | null | null | http://arxiv.org/pdf/1102.2467v1 | 2011-02-12T01:34:52Z | 2011-02-12T01:34:52Z | Universal Learning Theory | This encyclopedic article gives a mini-introduction into the theory of
universal learning, founded by Ray Solomonoff in the 1960s and significantly
developed and extended in the last decade. It explains the spirit of universal
learning, but necessarily glosses over technical subtleties.
| [
"Marcus Hutter",
"['Marcus Hutter']"
] |
math.ST cs.LG cs.SY math.OC stat.TH | null | 1102.2490 | null | null | http://arxiv.org/pdf/1102.2490v5 | 2013-08-29T15:37:53Z | 2011-02-12T10:03:21Z | The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond | This paper presents a finite-time analysis of the KL-UCB algorithm, an
online, horizon-free index policy for stochastic bandit problems. We prove two
distinct results: first, for arbitrary bounded rewards, the KL-UCB algorithm
satisfies a uniformly better regret bound than UCB or UCB2; second, in the
special case of Bernoulli rewards, it reaches the lower bound of Lai and
Robbins. Furthermore, we show that simple adaptations of the KL-UCB algorithm
are also optimal for specific classes of (possibly unbounded) rewards,
including those generated from exponential families of distributions. A
large-scale numerical study comparing KL-UCB with its main competitors (UCB,
UCB2, UCB-Tuned, UCB-V, DMED) shows that KL-UCB is remarkably efficient and
stable, including for short time horizons. KL-UCB is also the only method that
always performs better than the basic UCB policy. Our regret bounds rely on
deviations results of independent interest which are stated and proved in the
Appendix. As a by-product, we also obtain an improved regret bound for the
standard UCB algorithm.
| [
"['Aurélien Garivier' 'Olivier Cappé']",
"Aur\\'elien Garivier and Olivier Capp\\'e"
] |
cs.CV cs.AI cs.LG cs.NE | null | 1102.2739 | null | null | http://arxiv.org/pdf/1102.2739v1 | 2011-02-14T11:40:08Z | 2011-02-14T11:40:08Z | A General Framework for Development of the Cortex-like Visual Object
Recognition System: Waves of Spikes, Predictive Coding and Universal
Dictionary of Features | This study is focused on the development of the cortex-like visual object
recognition system. We propose a general framework, which consists of three
hierarchical levels (modules). These modules functionally correspond to the V1,
V4 and IT areas. Both bottom-up and top-down connections between the
hierarchical levels V4 and IT are employed. The higher the degree of matching
between the input and the preferred stimulus, the shorter the response time of
the neuron. Therefore information about a single stimulus is distributed in
time and is transmitted by the waves of spikes. The reciprocal connections and
waves of spikes implement predictive coding: an initial hypothesis is generated
on the basis of information delivered by the first wave of spikes and is tested
with the information carried by the consecutive waves. The development is
considered as extraction and accumulation of features in V4 and objects in IT.
Once stored a feature can be disposed, if rarely activated. This cause update
of feature repository. Consequently, objects in IT are also updated. This
illustrates the growing process and dynamical change of topological structures
of V4, IT and connections between these areas.
| [
"['Sergey S. Tarasenko']",
"Sergey S. Tarasenko"
] |
cs.LG | 10.1109/TNNLS.2012.2198240 | 1102.2808 | null | null | http://arxiv.org/abs/1102.2808v5 | 2012-09-03T02:17:30Z | 2011-02-14T15:53:06Z | Transductive Ordinal Regression | Ordinal regression is commonly formulated as a multi-class problem with
ordinal constraints. The challenge of designing accurate classifiers for
ordinal regression generally increases with the number of classes involved, due
to the large number of labeled patterns that are needed. The availability of
ordinal class labels, however, is often costly to calibrate or difficult to
obtain. Unlabeled patterns, on the other hand, often exist in much greater
abundance and are freely available. To take benefits from the abundance of
unlabeled patterns, we present a novel transductive learning paradigm for
ordinal regression in this paper, namely Transductive Ordinal Regression (TOR).
The key challenge of the present study lies in the precise estimation of both
the ordinal class label of the unlabeled data and the decision functions of the
ordinal classes, simultaneously. The core elements of the proposed TOR include
an objective function that caters to several commonly used loss functions
casted in transductive settings, for general ordinal regression. A label
swapping scheme that facilitates a strictly monotonic decrease in the objective
function value is also introduced. Extensive numerical studies on commonly used
benchmark datasets including the real world sentiment prediction problem are
then presented to showcase the characteristics and efficacies of the proposed
transductive ordinal regression. Further, comparisons to recent
state-of-the-art ordinal regression methods demonstrate the introduced
transductive learning paradigm for ordinal regression led to the robust and
improved performance.
| [
"['Chun-Wei Seah' 'Ivor W. Tsang' 'Yew-Soon Ong']",
"Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong"
] |
math.OC cs.LG cs.SY math.PR | null | 1102.2975 | null | null | http://arxiv.org/pdf/1102.2975v1 | 2011-02-15T06:12:44Z | 2011-02-15T06:12:44Z | Decentralized Restless Bandit with Multiple Players and Unknown Dynamics | We consider decentralized restless multi-armed bandit problems with unknown
dynamics and multiple players. The reward state of each arm transits according
to an unknown Markovian rule when it is played and evolves according to an
arbitrary unknown random process when it is passive. Players activating the
same arm at the same time collide and suffer from reward loss. The objective is
to maximize the long-term reward by designing a decentralized arm selection
policy to address unknown reward models and collisions among players. A
decentralized policy is constructed that achieves a regret with logarithmic
order when an arbitrary nontrivial bound on certain system parameters is known.
When no knowledge about the system is available, we extend the policy to
achieve a regret arbitrarily close to the logarithmic order. The result finds
applications in communication networks, financial investment, and industrial
engineering.
| [
"['Haoyang Liu' 'Keqin Liu' 'Qing Zhao']",
"Haoyang Liu, Keqin Liu, Qing Zhao"
] |
cs.IT cs.LG math.IT stat.ML | 10.1109/ISIT.2011.6033687 | 1102.3176 | null | null | http://arxiv.org/abs/1102.3176v3 | 2011-06-08T10:08:51Z | 2011-02-15T20:49:37Z | Selecting the rank of truncated SVD by Maximum Approximation Capacity | Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$
approximation of a given input matrix. Selecting the appropriate rank $k$
defines a critical model order choice in most applications of SVD. To obtain a
principled cut-off criterion for the spectrum, we convert the underlying
optimization problem into a noisy channel coding problem. The optimal
approximation capacity of this channel controls the appropriate strength of
regularization to suppress noise. In simulation experiments, this information
theoretic method to determine the optimal rank competes with state-of-the art
model selection techniques.
| [
"Mario Frank and Joachim M. Buhmann",
"['Mario Frank' 'Joachim M. Buhmann']"
] |
physics.data-an cond-mat.stat-mech cs.LG q-bio.NC q-bio.QM | 10.1103/PhysRevLett.106.090601 | 1102.3260 | null | null | http://arxiv.org/abs/1102.3260v1 | 2011-02-16T08:15:42Z | 2011-02-16T08:15:42Z | Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy
Data | We introduce a procedure to infer the interactions among a set of binary
variables, based on their sampled frequencies and pairwise correlations. The
algorithm builds the clusters of variables contributing most to the entropy of
the inferred Ising model, and rejects the small contributions due to the
sampling noise. Our procedure successfully recovers benchmark Ising models even
at criticality and in the low temperature phase, and is applied to
neurobiological data.
| [
"['Simona Cocco' 'Rémi Monasson']",
"Simona Cocco (LPS), R\\'emi Monasson (LPTENS)"
] |
math.OC cs.LG | 10.1109/TIT.2012.2198613 | 1102.3508 | null | null | http://arxiv.org/abs/1102.3508v1 | 2011-02-17T07:08:37Z | 2011-02-17T07:08:37Z | Online Learning of Rested and Restless Bandits | In this paper we study the online learning problem involving rested and
restless multiarmed bandits with multiple plays. The system consists of a
single player/user and a set of K finite-state discrete-time Markov chains
(arms) with unknown state spaces and statistics. At each time step the player
can play M arms. The objective of the user is to decide for each step which M
of the K arms to play over a sequence of trials so as to maximize its long term
reward. The restless multiarmed bandit is particularly relevant to the
application of opportunistic spectrum access (OSA), where a (secondary) user
has access to a set of K channels, each of time-varying condition as a result
of random fading and/or certain primary users' activities.
| [
"Cem Tekin and Mingyan Liu",
"['Cem Tekin' 'Mingyan Liu']"
] |
cs.IT cs.LG math.IT stat.ML | null | 1102.3887 | null | null | http://arxiv.org/pdf/1102.3887v1 | 2011-02-18T19:05:49Z | 2011-02-18T19:05:49Z | Active Clustering: Robust and Efficient Hierarchical Clustering using
Adaptively Selected Similarities | Hierarchical clustering based on pairwise similarities is a common tool used
in a broad range of scientific applications. However, in many problems it may
be expensive to obtain or compute similarities between the items to be
clustered. This paper investigates the hierarchical clustering of N items based
on a small subset of pairwise similarities, significantly less than the
complete set of N(N-1)/2 similarities. First, we show that if the intracluster
similarities exceed intercluster similarities, then it is possible to correctly
determine the hierarchical clustering from as few as 3N log N similarities. We
demonstrate this order of magnitude savings in the number of pairwise
similarities necessitates sequentially selecting which similarities to obtain
in an adaptive fashion, rather than picking them at random. We then propose an
active clustering method that is robust to a limited fraction of anomalous
similarities, and show how even in the presence of these noisy similarity
values we can resolve the hierarchical clustering using only O(N log^2 N)
pairwise similarities.
| [
"Brian Eriksson, Gautam Dasarathy, Aarti Singh, Robert Nowak",
"['Brian Eriksson' 'Gautam Dasarathy' 'Aarti Singh' 'Robert Nowak']"
] |
q-bio.GN cs.AI cs.LG q-bio.MN | null | 1102.3919 | null | null | http://arxiv.org/pdf/1102.3919v1 | 2011-02-18T21:01:38Z | 2011-02-18T21:01:38Z | Inferring Disease and Gene Set Associations with Rank Coherence in
Networks | A computational challenge to validate the candidate disease genes identified
in a high-throughput genomic study is to elucidate the associations between the
set of candidate genes and disease phenotypes. The conventional gene set
enrichment analysis often fails to reveal associations between disease
phenotypes and the gene sets with a short list of poorly annotated genes,
because the existing annotations of disease causative genes are incomplete. We
propose a network-based computational approach called rcNet to discover the
associations between gene sets and disease phenotypes. Assuming coherent
associations between the genes ranked by their relevance to the query gene set,
and the disease phenotypes ranked by their relevance to the hidden target
disease phenotypes of the query gene set, we formulate a learning framework
maximizing the rank coherence with respect to the known disease phenotype-gene
associations. An efficient algorithm coupling ridge regression with label
propagation, and two variants are introduced to find the optimal solution of
the framework. We evaluated the rcNet algorithms and existing baseline methods
with both leave-one-out cross-validation and a task of predicting recently
discovered disease-gene associations in OMIM. The experiments demonstrated that
the rcNet algorithms achieved the best overall rankings compared to the
baselines. To further validate the reproducibility of the performance, we
applied the algorithms to identify the target diseases of novel candidate
disease genes obtained from recent studies of GWAS, DNA copy number variation
analysis, and gene expression profiling. The algorithms ranked the target
disease of the candidate genes at the top of the rank list in many cases across
all the three case studies. The rcNet algorithms are available as a webtool for
disease and gene set association analysis at
http://compbio.cs.umn.edu/dgsa_rcNet.
| [
"['TaeHyun Hwang' 'Wei Zhang' 'Maoqiang Xie' 'Rui Kuang']",
"TaeHyun Hwang, Wei Zhang, Maoqiang Xie, Rui Kuang"
] |
cs.LG stat.ML | null | 1102.3923 | null | null | http://arxiv.org/pdf/1102.3923v2 | 2011-05-26T19:26:27Z | 2011-02-18T21:26:16Z | Concentration-Based Guarantees for Low-Rank Matrix Reconstruction | We consider the problem of approximately reconstructing a partially-observed,
approximately low-rank matrix. This problem has received much attention lately,
mostly using the trace-norm as a surrogate to the rank. Here we study low-rank
matrix reconstruction using both the trace-norm, as well as the less-studied
max-norm, and present reconstruction guarantees based on existing analysis on
the Rademacher complexity of the unit balls of these norms. We show how these
are superior in several ways to recently published guarantees based on
specialized analysis.
| [
"['Rina Foygel' 'Nathan Srebro']",
"Rina Foygel, Nathan Srebro"
] |
stat.ML cs.LG | 10.1109/JSTSP.2011.2159773 | 1102.3949 | null | null | http://arxiv.org/abs/1102.3949v2 | 2011-08-17T00:03:36Z | 2011-02-19T01:41:35Z | Sparse Signal Recovery with Temporally Correlated Source Vectors Using
Sparse Bayesian Learning | We address the sparse signal recovery problem in the context of multiple
measurement vectors (MMV) when elements in each nonzero row of the solution
matrix are temporally correlated. Existing algorithms do not consider such
temporal correlations and thus their performance degrades significantly with
the correlations. In this work, we propose a block sparse Bayesian learning
framework which models the temporal correlations. In this framework we derive
two sparse Bayesian learning (SBL) algorithms, which have superior recovery
performance compared to existing algorithms, especially in the presence of high
temporal correlations. Furthermore, our algorithms are better at handling
highly underdetermined problems and require less row-sparsity on the solution
matrix. We also provide analysis of the global and local minima of their cost
function, and show that the SBL cost function has the very desirable property
that the global minimum is at the sparsest solution to the MMV problem.
Extensive experiments also provide some interesting results that motivate
future theoretical research on the MMV model.
| [
"Zhilin Zhang and Bhaskar D. Rao",
"['Zhilin Zhang' 'Bhaskar D. Rao']"
] |
cs.LG cs.CR | null | 1102.4021 | null | null | http://arxiv.org/pdf/1102.4021v2 | 2011-09-18T05:37:43Z | 2011-02-19T20:40:56Z | Privacy Preserving Spam Filtering | Email is a private medium of communication, and the inherent privacy
constraints form a major obstacle in developing effective spam filtering
methods which require access to a large amount of email data belonging to
multiple users. To mitigate this problem, we envision a privacy preserving spam
filtering system, where the server is able to train and evaluate a logistic
regression based spam classifier on the combined email data of all users
without being able to observe any emails using primitives such as homomorphic
encryption and randomization. We analyze the protocols for correctness and
security, and perform experiments of a prototype system on a large scale spam
filtering task.
State of the art spam filters often use character n-grams as features which
result in large sparse data representation, which is not feasible to be used
directly with our training and evaluation protocols. We explore various data
independent dimensionality reduction which decrease the running time of the
protocol making it feasible to use in practice while achieving high accuracy.
| [
"['Manas A. Pathak' 'Mehrbod Sharifi' 'Bhiksha Raj']",
"Manas A. Pathak, Mehrbod Sharifi, Bhiksha Raj"
] |
cs.LG cs.DS | null | 1102.4240 | null | null | http://arxiv.org/pdf/1102.4240v1 | 2011-02-21T14:48:20Z | 2011-02-21T14:48:20Z | Sparse neural networks with large learning diversity | Coded recurrent neural networks with three levels of sparsity are introduced.
The first level is related to the size of messages, much smaller than the
number of available neurons. The second one is provided by a particular coding
rule, acting as a local constraint in the neural activity. The third one is a
characteristic of the low final connection density of the network after the
learning phase. Though the proposed network is very simple since it is based on
binary neurons and binary connections, it is able to learn a large number of
messages and recall them, even in presence of strong erasures. The performance
of the network is assessed as a classifier and as an associative memory.
| [
"Vincent Gripon and Claude Berrou",
"['Vincent Gripon' 'Claude Berrou']"
] |
cs.CR cs.LG | null | 1102.4374 | null | null | http://arxiv.org/pdf/1102.4374v1 | 2011-02-22T00:11:14Z | 2011-02-22T00:11:14Z | Link Prediction by De-anonymization: How We Won the Kaggle Social
Network Challenge | This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.
| [
"['Arvind Narayanan' 'Elaine Shi' 'Benjamin I. P. Rubinstein']",
"Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein"
] |
cs.LG cs.GT math.OC | null | 1102.4442 | null | null | http://arxiv.org/pdf/1102.4442v1 | 2011-02-22T09:56:28Z | 2011-02-22T09:56:28Z | Internal Regret with Partial Monitoring. Calibration-Based Optimal
Algorithms | We provide consistent random algorithms for sequential decision under partial
monitoring, i.e. when the decision maker does not observe the outcomes but
receives instead random feedback signals. Those algorithms have no internal
regret in the sense that, on the set of stages where the decision maker chose
his action according to a given law, the average payoff could not have been
improved in average by using any other fixed law.
They are based on a generalization of calibration, no longer defined in terms
of a Voronoi diagram but instead of a Laguerre diagram (a more general
concept). This allows us to bound, for the first time in this general
framework, the expected average internal -- as well as the usual external --
regret at stage $n$ by $O(n^{-1/3})$, which is known to be optimal.
| [
"['Vianney Perchet']",
"Vianney Perchet"
] |
stat.ML cs.IT cs.LG math.IT | 10.1214/12-AOS1000 | 1102.4807 | null | null | http://arxiv.org/abs/1102.4807v3 | 2012-03-06T06:59:59Z | 2011-02-23T18:02:53Z | Noisy matrix decomposition via convex relaxation: Optimal rates in high
dimensions | We analyze a class of estimators based on convex relaxation for solving
high-dimensional matrix decomposition problems. The observations are noisy
realizations of a linear transformation $\mathfrak{X}$ of the sum of an
approximately) low rank matrix $\Theta^\star$ with a second matrix
$\Gamma^\star$ endowed with a complementary form of low-dimensional structure;
this set-up includes many statistical models of interest, including factor
analysis, multi-task regression, and robust covariance estimation. We derive a
general theorem that bounds the Frobenius norm error for an estimate of the
pair $(\Theta^\star, \Gamma^\star)$ obtained by solving a convex optimization
problem that combines the nuclear norm with a general decomposable regularizer.
Our results utilize a "spikiness" condition that is related to but milder than
singular vector incoherence. We specialize our general result to two cases that
have been studied in past work: low rank plus an entrywise sparse matrix, and
low rank plus a columnwise sparse matrix. For both models, our theory yields
non-asymptotic Frobenius error bounds for both deterministic and stochastic
noise matrices, and applies to matrices $\Theta^\star$ that can be exactly or
approximately low rank, and matrices $\Gamma^\star$ that can be exactly or
approximately sparse. Moreover, for the case of stochastic noise matrices and
the identity observation operator, we establish matching lower bounds on the
minimax error. The sharpness of our predictions is confirmed by numerical
simulations.
| [
"['Alekh Agarwal' 'Sahand N. Negahban' 'Martin J. Wainwright']",
"Alekh Agarwal and Sahand N. Negahban and Martin J. Wainwright"
] |
stat.ML cs.LG cs.SY math.OC stat.AP | null | 1102.5288 | null | null | http://arxiv.org/pdf/1102.5288v2 | 2011-09-09T19:06:10Z | 2011-02-25T17:13:00Z | Sparse Bayesian Methods for Low-Rank Matrix Estimation | Recovery of low-rank matrices has recently seen significant activity in many
areas of science and engineering, motivated by recent theoretical results for
exact reconstruction guarantees and interesting practical applications. A
number of methods have been developed for this recovery problem. However, a
principled method for choosing the unknown target rank is generally not
provided. In this paper, we present novel recovery algorithms for estimating
low-rank matrices in matrix completion and robust principal component analysis
based on sparse Bayesian learning (SBL) principles. Starting from a matrix
factorization formulation and enforcing the low-rank constraint in the
estimates as a sparsity constraint, we develop an approach that is very
effective in determining the correct rank while providing high recovery
performance. We provide connections with existing methods in other similar
problems and empirical results and comparisons with current state-of-the-art
methods that illustrate the effectiveness of this approach.
| [
"S. Derin Babacan, Martin Luessi, Rafael Molina, Aggelos K. Katsaggelos",
"['S. Derin Babacan' 'Martin Luessi' 'Rafael Molina'\n 'Aggelos K. Katsaggelos']"
] |
cond-mat.stat-mech cs.IT cs.LG math.IT | null | 1102.5396 | null | null | http://arxiv.org/pdf/1102.5396v1 | 2011-02-26T09:31:25Z | 2011-02-26T09:31:25Z | Deformed Statistics Free Energy Model for Source Separation using
Unsupervised Learning | A generalized-statistics variational principle for source separation is
formulated by recourse to Tsallis' entropy subjected to the additive duality
and employing constraints described by normal averages. The variational
principle is amalgamated with Hopfield-like learning rules resulting in an
unsupervised learning model. The update rules are formulated with the aid of
q-deformed calculus. Numerical examples exemplify the efficacy of this model.
| [
"R. C. Venkatesan and A. Plastino",
"['R. C. Venkatesan' 'A. Plastino']"
] |
cs.AI cs.LG | null | 1102.5561 | null | null | http://arxiv.org/pdf/1102.5561v2 | 2011-03-01T07:35:59Z | 2011-02-27T23:47:13Z | Decision Making Agent Searching for Markov Models in Near-Deterministic
World | Reinforcement learning has solid foundations, but becomes inefficient in
partially observed (non-Markovian) environments. Thus, a learning agent -born
with a representation and a policy- might wish to investigate to what extent
the Markov property holds. We propose a learning architecture that utilizes
combinatorial policy optimization to overcome non-Markovity and to develop
efficient behaviors, which are easy to inherit, tests the Markov property of
the behavioral states, and corrects against non-Markovity by running a
deterministic factored Finite State Model, which can be learned. We illustrate
the properties of architecture in the near deterministic Ms. Pac-Man game. We
analyze the architecture from the point of view of evolutionary, individual,
and social learning.
| [
"['Gabor Matuz' 'Andras Lorincz']",
"Gabor Matuz and Andras Lorincz"
] |
cs.IT cs.LG math.IT | 10.1109/WCNC.2011.5779375 | 1102.5593 | null | null | http://arxiv.org/abs/1102.5593v2 | 2011-03-03T13:10:48Z | 2011-02-28T04:16:24Z | Low Complexity Kolmogorov-Smirnov Modulation Classification | Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness
of fit, is applied for automatic modulation classification (AMC) in this paper.
The basic procedure involves computing the empirical cumulative distribution
function (ECDF) of some decision statistic derived from the received signal,
and comparing it with the CDFs of the signal under each candidate modulation
format. The K-S-based modulation classifier is first developed for AWGN
channel, then it is applied to OFDM-SDMA systems to cancel multiuser
interference. Regarding the complexity issue of K-S modulation classification,
we propose a low-complexity method based on the robustness of the K-S
classifier. Extensive simulation results demonstrate that compared with the
traditional cumulant-based classifiers, the proposed K-S classifier offers
superior classification performance and requires less number of signal samples
(thus is fast).
| [
"Fanggang Wang, Rongtao Xu, Zhangdui Zhong",
"['Fanggang Wang' 'Rongtao Xu' 'Zhangdui Zhong']"
] |
cs.NA cs.LG | null | 1102.5597 | null | null | http://arxiv.org/pdf/1102.5597v1 | 2011-02-28T05:26:58Z | 2011-02-28T05:26:58Z | Fast and Faster: A Comparison of Two Streamed Matrix Decomposition
Algorithms | With the explosion of the size of digital dataset, the limiting factor for
decomposition algorithms is the \emph{number of passes} over the input, as the
input is often stored out-of-core or even off-site. Moreover, we're only
interested in algorithms that operate in \emph{constant memory} w.r.t. to the
input size, so that arbitrarily large input can be processed. In this paper, we
present a practical comparison of two such algorithms: a distributed method
that operates in a single pass over the input vs. a streamed two-pass
stochastic algorithm. The experiments track the effect of distributed
computing, oversampling and memory trade-offs on the accuracy and performance
of the two algorithms. To ensure meaningful results, we choose the input to be
a real dataset, namely the whole of the English Wikipedia, in the application
settings of Latent Semantic Analysis.
| [
"['Radim Řeh{ů}řek']",
"Radim \\v{R}eh{\\r{u}}\\v{r}ek"
] |
cs.IR cs.LG | 10.5121/ijmit.2011.3104 | 1102.5728 | null | null | http://arxiv.org/abs/1102.5728v1 | 2011-02-28T18:33:09Z | 2011-02-28T18:33:09Z | Named Entity Recognition Using Web Document Corpus | This paper introduces a named entity recognition approach in textual corpus.
This Named Entity (NE) can be a named: location, person, organization, date,
time, etc., characterized by instances. A NE is found in texts accompanied by
contexts: words that are left or right of the NE. The work mainly aims at
identifying contexts inducing the NE's nature. As such, The occurrence of the
word "President" in a text, means that this word or context may be followed by
the name of a president as President "Obama". Likewise, a word preceded by the
string "footballer" induces that this is the name of a footballer. NE
recognition may be viewed as a classification method, where every word is
assigned to a NE class, regarding the context. The aim of this study is then to
identify and classify the contexts that are most relevant to recognize a NE,
those which are frequently found with the NE. A learning approach using
training corpus: web documents, constructed from learning examples is then
suggested. Frequency representations and modified tf-idf representations are
used to calculate the context weights associated to context frequency, learning
example frequency, and document frequency in the corpus.
| [
"['Wahiba Ben Abdessalem Karaa']",
"Wahiba Ben Abdessalem Karaa"
] |
stat.ML cs.LG math.ST stat.TH | null | 1102.5750 | null | null | http://arxiv.org/pdf/1102.5750v1 | 2011-02-28T19:31:41Z | 2011-02-28T19:31:41Z | Neyman-Pearson classification, convexity and stochastic constraints | Motivated by problems of anomaly detection, this paper implements the
Neyman-Pearson paradigm to deal with asymmetric errors in binary classification
with a convex loss. Given a finite collection of classifiers, we combine them
and obtain a new classifier that satisfies simultaneously the two following
properties with high probability: (i) its probability of type I error is below
a pre-specified level and (ii), it has probability of type II error close to
the minimum possible. The proposed classifier is obtained by solving an
optimization problem with an empirical objective and an empirical constraint.
New techniques to handle such problems are developed and have consequences on
chance constrained programming.
| [
"Philippe Rigollet and Xin Tong",
"['Philippe Rigollet' 'Xin Tong']"
] |
cs.DC cs.CR cs.LG | null | 1103.0086 | null | null | http://arxiv.org/pdf/1103.0086v1 | 2011-03-01T06:03:15Z | 2011-03-01T06:03:15Z | A generic trust framework for large-scale open systems using machine
learning | In many large scale distributed systems and on the web, agents need to
interact with other unknown agents to carry out some tasks or transactions. The
ability to reason about and assess the potential risks in carrying out such
transactions is essential for providing a safe and reliable environment. A
traditional approach to reason about the trustworthiness of a transaction is to
determine the trustworthiness of the specific agent involved, derived from the
history of its behavior. As a departure from such traditional trust models, we
propose a generic, machine learning approach based trust framework where an
agent uses its own previous transactions (with other agents) to build a
knowledge base, and utilize this to assess the trustworthiness of a transaction
based on associated features, which are capable of distinguishing successful
transactions from unsuccessful ones. These features are harnessed using
appropriate machine learning algorithms to extract relationships between the
potential transaction and previous transactions. The trace driven experiments
using real auction dataset show that this approach provides good accuracy and
is highly efficient compared to other trust mechanisms, especially when
historical information of the specific agent is rare, incomplete or inaccurate.
| [
"Xin Liu and Gilles Tredan and Anwitaman Datta",
"['Xin Liu' 'Gilles Tredan' 'Anwitaman Datta']"
] |
cs.LG stat.ML | null | 1103.0102 | null | null | http://arxiv.org/pdf/1103.0102v2 | 2011-03-03T00:00:13Z | 2011-03-01T08:15:28Z | Multi-label Learning via Structured Decomposition and Group Sparsity | In multi-label learning, each sample is associated with several labels.
Existing works indicate that exploring correlations between labels improve the
prediction performance. However, embedding the label correlations into the
training process significantly increases the problem size. Moreover, the
mapping of the label structure in the feature space is not clear. In this
paper, we propose a novel multi-label learning method "Structured Decomposition
+ Group Sparsity (SDGS)". In SDGS, we learn a feature subspace for each label
from the structured decomposition of the training data, and predict the labels
of a new sample from its group sparse representation on the multi-subspace
obtained from the structured decomposition. In particular, in the training
stage, we decompose the data matrix $X\in R^{n\times p}$ as
$X=\sum_{i=1}^kL^i+S$, wherein the rows of $L^i$ associated with samples that
belong to label $i$ are nonzero and consist a low-rank matrix, while the other
rows are all-zeros, the residual $S$ is a sparse matrix. The row space of $L_i$
is the feature subspace corresponding to label $i$. This decomposition can be
efficiently obtained via randomized optimization. In the prediction stage, we
estimate the group sparse representation of a new sample on the multi-subspace
via group \emph{lasso}. The nonzero representation coefficients tend to
concentrate on the subspaces of labels that the sample belongs to, and thus an
effective prediction can be obtained. We evaluate SDGS on several real datasets
and compare it with popular methods. Results verify the effectiveness and
efficiency of SDGS.
| [
"Tianyi Zhou and Dacheng Tao",
"['Tianyi Zhou' 'Dacheng Tao']"
] |
cs.LG cs.CL | null | 1103.0398 | null | null | http://arxiv.org/pdf/1103.0398v1 | 2011-03-02T11:34:50Z | 2011-03-02T11:34:50Z | Natural Language Processing (almost) from Scratch | We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements.
| [
"['Ronan Collobert' 'Jason Weston' 'Leon Bottou' 'Michael Karlen'\n 'Koray Kavukcuoglu' 'Pavel Kuksa']",
"Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray\n Kavukcuoglu, Pavel Kuksa"
] |
cs.LG | null | 1103.0598 | null | null | http://arxiv.org/pdf/1103.0598v1 | 2011-03-03T02:46:51Z | 2011-03-03T02:46:51Z | Learning transformed product distributions | We consider the problem of learning an unknown product distribution $X$ over
$\{0,1\}^n$ using samples $f(X)$ where $f$ is a \emph{known} transformation
function. Each choice of a transformation function $f$ specifies a learning
problem in this framework.
Information-theoretic arguments show that for every transformation function
$f$ the corresponding learning problem can be solved to accuracy $\eps$, using
$\tilde{O}(n/\eps^2)$ examples, by a generic algorithm whose running time may
be exponential in $n.$ We show that this learning problem can be
computationally intractable even for constant $\eps$ and rather simple
transformation functions. Moreover, the above sample complexity bound is nearly
optimal for the general problem, as we give a simple explicit linear
transformation function $f(x)=w \cdot x$ with integer weights $w_i \leq n$ and
prove that the corresponding learning problem requires $\Omega(n)$ samples.
As our main positive result we give a highly efficient algorithm for learning
a sum of independent unknown Bernoulli random variables, corresponding to the
transformation function $f(x)= \sum_{i=1}^n x_i$. Our algorithm learns to
$\eps$-accuracy in poly$(n)$ time, using a surprising poly$(1/\eps)$ number of
samples that is independent of $n.$ We also give an efficient algorithm that
uses $\log n \cdot \poly(1/\eps)$ samples but has running time that is only
$\poly(\log n, 1/\eps).$
| [
"['Constantinos Daskalakis' 'Ilias Diakonikolas' 'Rocco A. Servedio']",
"Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio"
] |
cs.LG cs.IT math.IT stat.ML | 10.1109/TSP.2011.2165952 | 1103.0769 | null | null | http://arxiv.org/abs/1103.0769v2 | 2011-09-07T00:03:45Z | 2011-03-03T20:21:28Z | Sparse Volterra and Polynomial Regression Models: Recoverability and
Estimation | Volterra and polynomial regression models play a major role in nonlinear
system identification and inference tasks. Exciting applications ranging from
neuroscience to genome-wide association analysis build on these models with the
additional requirement of parsimony. This requirement has high interpretative
value, but unfortunately cannot be met by least-squares based or kernel
regression methods. To this end, compressed sampling (CS) approaches, already
successful in linear regression settings, can offer a viable alternative. The
viability of CS for sparse Volterra and polynomial models is the core theme of
this work. A common sparse regression task is initially posed for the two
models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type
algorithm is developed for sparse polynomial regressions. The identifiability
of polynomial models is critically challenged by dimensionality. However,
following the CS principle, when these models are sparse, they could be
recovered by far fewer measurements. To quantify the sufficient number of
measurements for a given level of sparsity, restricted isometry properties
(RIP) are investigated in commonly met polynomial regression settings,
generalizing known results for their linear counterparts. The merits of the
novel (weighted) adaptive CS algorithms to sparse polynomial modeling are
verified through synthetic as well as real data tests for genotype-phenotype
analysis.
| [
"['Vassilis Kekatos' 'Georgios B. Giannakis']",
"Vassilis Kekatos and Georgios B. Giannakis"
] |
cs.LG cs.CL | null | 1103.0890 | null | null | http://arxiv.org/pdf/1103.0890v2 | 2013-05-04T13:57:32Z | 2011-03-04T13:08:59Z | Efficient Multi-Template Learning for Structured Prediction | Conditional random field (CRF) and Structural Support Vector Machine
(Structural SVM) are two state-of-the-art methods for structured prediction
which captures the interdependencies among output variables. The success of
these methods is attributed to the fact that their discriminative models are
able to account for overlapping features on the whole input observations. These
features are usually generated by applying a given set of templates on labeled
data, but improper templates may lead to degraded performance. To alleviate
this issue, in this paper, we propose a novel multiple template learning
paradigm to learn structured prediction and the importance of each template
simultaneously, so that hundreds of arbitrary templates could be added into the
learning model without caution. This paradigm can be formulated as a special
multiple kernel learning problem with exponential number of constraints. Then
we introduce an efficient cutting plane algorithm to solve this problem in the
primal, and its convergence is presented. We also evaluate the proposed
learning paradigm on two widely-studied structured prediction tasks,
\emph{i.e.} sequence labeling and dependency parsing. Extensive experimental
results show that the proposed method outperforms CRFs and Structural SVMs due
to exploiting the importance of each template. Our complexity analysis and
empirical results also show that our proposed method is more efficient than
OnlineMKL on very sparse and high-dimensional data. We further extend this
paradigm for structured prediction using generalized $p$-block norm
regularization with $p>1$, and experiments show competitive performances when
$p \in [1,2)$.
| [
"['Qi Mao' 'Ivor W. Tsang']",
"Qi Mao, Ivor W. Tsang"
] |
stat.ML cs.LG math.PR | null | 1103.0941 | null | null | http://arxiv.org/pdf/1103.0941v1 | 2011-03-04T16:29:04Z | 2011-03-04T16:29:04Z | Estimating $\beta$-mixing coefficients | The literature on statistical learning for time series assumes the asymptotic
independence or ``mixing' of the data-generating process. These mixing
assumptions are never tested, nor are there methods for estimating mixing rates
from data. We give an estimator for the $\beta$-mixing rate based on a single
stationary sample path and show it is $L_1$-risk consistent.
| [
"Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish (Carnegie\n Mellon University)",
"['Daniel J. McDonald' 'Cosma Rohilla Shalizi' 'Mark Schervish']"
] |
stat.ML cs.LG | null | 1103.0942 | null | null | http://arxiv.org/pdf/1103.0942v2 | 2011-06-03T19:08:19Z | 2011-03-04T16:38:55Z | Generalization error bounds for stationary autoregressive models | We derive generalization error bounds for stationary univariate
autoregressive (AR) models. We show that imposing stationarity is enough to
control the Gaussian complexity without further regularization. This lets us
use structural risk minimization for model selection. We demonstrate our
methods by predicting interest rate movements.
| [
"Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish (Carnegie\n Mellon University)",
"['Daniel J. McDonald' 'Cosma Rohilla Shalizi' 'Mark Schervish']"
] |
stat.ML cs.LG physics.data-an stat.ME | null | 1103.0949 | null | null | http://arxiv.org/pdf/1103.0949v2 | 2011-06-28T23:25:41Z | 2011-03-04T17:04:20Z | Adapting to Non-stationarity with Growing Expert Ensembles | When dealing with time series with complex non-stationarities, low
retrospective regret on individual realizations is a more appropriate goal than
low prospective risk in expectation. Online learning algorithms provide
powerful guarantees of this form, and have often been proposed for use with
non-stationary processes because of their ability to switch between different
forecasters or ``experts''. However, existing methods assume that the set of
experts whose forecasts are to be combined are all given at the start, which is
not plausible when dealing with a genuinely historical or evolutionary system.
We show how to modify the ``fixed shares'' algorithm for tracking the best
expert to cope with a steadily growing set of experts, obtained by fitting new
models to new data as it becomes available, and obtain regret bounds for the
growing ensemble.
| [
"Cosma Rohilla Shalizi, Abigail Z. Jacobs, Kristina Lisa Klinkner,\n Aaron Clauset",
"['Cosma Rohilla Shalizi' 'Abigail Z. Jacobs' 'Kristina Lisa Klinkner'\n 'Aaron Clauset']"
] |
cs.LG | 10.1109/TPAMI.2012.266 | 1103.1013 | null | null | http://arxiv.org/abs/1103.1013v2 | 2013-05-04T14:48:06Z | 2011-03-05T07:10:41Z | A Feature Selection Method for Multivariate Performance Measures | Feature selection with specific multivariate performance measures is the key
to the success of many applications, such as image retrieval and text
classification. The existing feature selection methods are usually designed for
classification error. In this paper, we propose a generalized sparse
regularizer. Based on the proposed regularizer, we present a unified feature
selection framework for general loss functions. In particular, we study the
novel feature selection paradigm by optimizing multivariate performance
measures. The resultant formulation is a challenging problem for
high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed
to solve this problem, and the convergence is presented. In addition, we adapt
the proposed method to optimize multivariate measures for multiple instance
learning problems. The analyses by comparing with the state-of-the-art feature
selection methods show that the proposed method is superior to others.
Extensive experiments on large-scale and high-dimensional real world datasets
show that the proposed method outperforms $l_1$-SVM and SVM-RFE when choosing a
small subset of features, and achieves significantly improved performances over
SVM$^{perf}$ in terms of $F_1$-score.
| [
"['Qi Mao' 'Ivor W. Tsang']",
"Qi Mao, Ivor W. Tsang"
] |
math.ST cs.LG cs.SY math.OC math.PR stat.TH | 10.1007/s10208-012-9129-5 | 1103.1417 | null | null | http://arxiv.org/abs/1103.1417v4 | 2012-11-21T02:31:50Z | 2011-03-08T02:34:40Z | Localization from Incomplete Noisy Distance Measurements | We consider the problem of positioning a cloud of points in the Euclidean
space $\mathbb{R}^d$, using noisy measurements of a subset of pairwise
distances. This task has applications in various areas, such as sensor network
localization and reconstruction of protein conformations from NMR measurements.
Also, it is closely related to dimensionality reduction problems and manifold
learning, where the goal is to learn the underlying global geometry of a data
set using local (or partial) metric information. Here we propose a
reconstruction algorithm based on semidefinite programming. For a random
geometric graph model and uniformly bounded noise, we provide a precise
characterization of the algorithm's performance: In the noiseless case, we find
a radius $r_0$ beyond which the algorithm reconstructs the exact positions (up
to rigid transformations). In the presence of noise, we obtain upper and lower
bounds on the reconstruction error that match up to a factor that depends only
on the dimension $d$, and the average degree of the nodes in the graph.
| [
"Adel Javanmard, Andrea Montanari",
"['Adel Javanmard' 'Andrea Montanari']"
] |
cs.CG cs.LG | null | 1103.1625 | null | null | http://arxiv.org/pdf/1103.1625v2 | 2011-03-09T23:22:09Z | 2011-03-08T20:50:55Z | A Gentle Introduction to the Kernel Distance | This document reviews the definition of the kernel distance, providing a
gentle introduction tailored to a reader with background in theoretical
computer science, but limited exposure to technology more common to machine
learning, functional analysis and geometric measure theory. The key aspect of
the kernel distance developed here is its interpretation as an L_2 distance
between probability measures or various shapes (e.g. point sets, curves,
surfaces) embedded in a vector space (specifically an RKHS). This structure
enables several elegant and efficient solutions to data analysis problems. We
conclude with a glimpse into the mathematical underpinnings of this measure,
highlighting its recent independent evolution in two separate fields.
| [
"['Jeff M. Phillips' 'Suresh Venkatasubramanian']",
"Jeff M. Phillips, Suresh Venkatasubramanian"
] |
cs.IT cs.LG math.IT math.ST q-fin.ST stat.ML stat.TH | null | 1103.1689 | null | null | http://arxiv.org/pdf/1103.1689v1 | 2011-03-09T02:03:17Z | 2011-03-09T02:03:17Z | Information Theoretic Limits on Learning Stochastic Differential
Equations | Consider the problem of learning the drift coefficient of a stochastic
differential equation from a sample path. In this paper, we assume that the
drift is parametrized by a high dimensional vector. We address the question of
how long the system needs to be observed in order to learn this vector of
parameters. We prove a general lower bound on this time complexity by using a
characterization of mutual information as time integral of conditional
variance, due to Kadota, Zakai, and Ziv. This general lower bound is applied to
specific classes of linear and non-linear stochastic differential equations. In
the linear case, the problem under consideration is the one of learning a
matrix of interaction coefficients. We evaluate our lower bound for ensembles
of sparse and dense random matrices. The resulting estimates match the
qualitative behavior of upper bounds achieved by computationally efficient
procedures.
| [
"['José Bento' 'Morteza Ibrahimi' 'Andrea Montanari']",
"Jos\\'e Bento and Morteza Ibrahimi and Andrea Montanari"
] |
cs.LG cs.DC stat.ML | 10.1109/ICDM.2011.39 | 1103.2068 | null | null | http://arxiv.org/abs/1103.2068v2 | 2011-09-08T16:20:45Z | 2011-03-10T16:15:42Z | COMET: A Recipe for Learning and Using Large Ensembles on Massive Data | COMET is a single-pass MapReduce algorithm for learning on large-scale data.
It builds multiple random forest ensembles on distributed blocks of data and
merges them into a mega-ensemble. This approach is appropriate when learning
from massive-scale data that is too large to fit on a single machine. To get
the best accuracy, IVoting should be used instead of bagging to generate the
training subset for each decision tree in the random forest. Experiments with
two large datasets (5GB and 50GB compressed) show that COMET compares favorably
(in both accuracy and training time) to learning on a subsample of data using a
serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble
evaluation which dynamically decides how many ensemble members to evaluate per
data point; this can reduce evaluation cost by 100X or more.
| [
"['Justin D. Basilico' 'M. Arthur Munson' 'Tamara G. Kolda'\n 'Kevin R. Dixon' 'W. Philip Kegelmeyer']",
"Justin D. Basilico and M. Arthur Munson and Tamara G. Kolda and Kevin\n R. Dixon and W. Philip Kegelmeyer"
] |
cs.LG cs.GT cs.SY math.OC | null | 1103.2491 | null | null | http://arxiv.org/pdf/1103.2491v1 | 2011-03-13T03:18:55Z | 2011-03-13T03:18:55Z | Heterogeneous Learning in Zero-Sum Stochastic Games with Incomplete
Information | Learning algorithms are essential for the applications of game theory in a
networking environment. In dynamic and decentralized settings where the
traffic, topology and channel states may vary over time and the communication
between agents is impractical, it is important to formulate and study games of
incomplete information and fully distributed learning algorithms which for each
agent requires a minimal amount of information regarding the remaining agents.
In this paper, we address this major challenge and introduce heterogeneous
learning schemes in which each agent adopts a distinct learning pattern in the
context of games with incomplete information. We use stochastic approximation
techniques to show that the heterogeneous learning schemes can be studied in
terms of their deterministic ordinary differential equation (ODE) counterparts.
Depending on the learning rates of the players, these ODEs could be different
from the standard replicator dynamics, (myopic) best response (BR) dynamics,
logit dynamics, and fictitious play dynamics. We apply the results to a class
of security games in which the attacker and the defender adopt different
learning schemes due to differences in their rationality levels and the
information they acquire.
| [
"Quanyan Zhu, Hamidou Tembine and Tamer Basar",
"['Quanyan Zhu' 'Hamidou Tembine' 'Tamer Basar']"
] |
cs.LG cs.IR cs.SD | null | 1103.2832 | null | null | http://arxiv.org/pdf/1103.2832v1 | 2011-03-15T02:39:31Z | 2011-03-15T02:39:31Z | Autotagging music with conditional restricted Boltzmann machines | This paper describes two applications of conditional restricted Boltzmann
machines (CRBMs) to the task of autotagging music. The first consists of
training a CRBM to predict tags that a user would apply to a clip of a song
based on tags already applied by other users. By learning the relationships
between tags, this model is able to pre-process training data to significantly
improve the performance of a support vector machine (SVM) autotagging. The
second is the use of a discriminative RBM, a type of CRBM, to autotag music. By
simultaneously exploiting the relationships among tags and between tags and
audio-based features, this model is able to significantly outperform SVMs,
logistic regression, and multi-layer perceptrons. In order to be applied to
this problem, the discriminative RBM was generalized to the multi-label setting
and four different learning algorithms for it were evaluated, the first such
in-depth analysis of which we are aware.
| [
"['Michael Mandel' 'Razvan Pascanu' 'Hugo Larochelle' 'Yoshua Bengio']",
"Michael Mandel, Razvan Pascanu, Hugo Larochelle and Yoshua Bengio"
] |
cs.LG stat.ML | null | 1103.3095 | null | null | http://arxiv.org/pdf/1103.3095v1 | 2011-03-16T04:54:58Z | 2011-03-16T04:54:58Z | A note on active learning for smooth problems | We show that the disagreement coefficient of certain smooth hypothesis
classes is $O(m)$, where $m$ is the dimension of the hypothesis space, thereby
answering a question posed in \cite{friedman09}.
| [
"Satyaki Mahalanabis",
"['Satyaki Mahalanabis']"
] |
cs.GT cs.LG cs.NI | 10.1109/JSAC.2012.1201xx | 1103.3541 | null | null | http://arxiv.org/abs/1103.3541v2 | 2011-11-16T13:34:16Z | 2011-03-18T00:40:42Z | Distributed Learning Policies for Power Allocation in Multiple Access
Channels | We analyze the problem of distributed power allocation for orthogonal
multiple access channels by considering a continuous non-cooperative game whose
strategy space represents the users' distribution of transmission power over
the network's channels. When the channels are static, we find that this game
admits an exact potential function and this allows us to show that it has a
unique equilibrium almost surely. Furthermore, using the game's potential
property, we derive a modified version of the replicator dynamics of
evolutionary game theory which applies to this continuous game, and we show
that if the network's users employ a distributed learning scheme based on these
dynamics, then they converge to equilibrium exponentially quickly. On the other
hand, a major challenge occurs if the channels do not remain static but
fluctuate stochastically over time, following a stationary ergodic process. In
that case, the associated ergodic game still admits a unique equilibrium, but
the learning analysis becomes much more complicated because the replicator
dynamics are no longer deterministic. Nonetheless, by employing results from
the theory of stochastic approximation, we show that users still converge to
the game's unique equilibrium.
Our analysis hinges on a game-theoretical result which is of independent
interest: in finite player games which admit a (possibly nonlinear) convex
potential function, the replicator dynamics (suitably modified to account for
nonlinear payoffs) converge to an eps-neighborhood of an equilibrium at time of
order O(log(1/eps)).
| [
"['Panayotis Mertikopoulos' 'Elena V. Belmega' 'Aris L. Moustakas'\n 'Samson Lasaulce']",
"Panayotis Mertikopoulos and Elena V. Belmega and Aris L. Moustakas and\n Samson Lasaulce"
] |
cs.IR cs.AI cs.LG | null | 1103.3735 | null | null | http://arxiv.org/pdf/1103.3735v1 | 2011-03-19T00:08:45Z | 2011-03-19T00:08:45Z | Refining Recency Search Results with User Click Feedback | Traditional machine-learned ranking systems for web search are often trained
to capture stationary relevance of documents to queries, which has limited
ability to track non-stationary user intention in a timely manner. In recency
search, for instance, the relevance of documents to a query on breaking news
often changes significantly over time, requiring effective adaptation to user
intention. In this paper, we focus on recency search and study a number of
algorithms to improve ranking results by leveraging user click feedback. Our
contributions are three-fold. First, we use real search sessions collected in a
random exploration bucket for \emph{reliable} offline evaluation of these
algorithms, which provides an unbiased comparison across algorithms without
online bucket tests. Second, we propose a re-ranking approach to improve search
results for recency queries using user clicks. Third, our empirical comparison
of a dozen algorithms on real-life search data suggests importance of a few
algorithmic choices in these applications, including generalization across
different query-document pairs, specialization to popular queries, and
real-time adaptation of user clicks.
| [
"['Taesup Moon' 'Wei Chu' 'Lihong Li' 'Zhaohui Zheng' 'Yi Chang']",
"Taesup Moon and Wei Chu and Lihong Li and Zhaohui Zheng and Yi Chang"
] |
cond-mat.dis-nn cs.LG physics.bio-ph | 10.1088/1742-6596/297/1/012012 | 1103.3787 | null | null | http://arxiv.org/abs/1103.3787v1 | 2011-03-19T14:57:03Z | 2011-03-19T14:57:03Z | Pattern-recalling processes in quantum Hopfield networks far from
saturation | As a mathematical model of associative memories, the Hopfield model was now
well-established and a lot of studies to reveal the pattern-recalling process
have been done from various different approaches. As well-known, a single
neuron is itself an uncertain, noisy unit with a finite unnegligible error in
the input-output relation. To model the situation artificially, a kind of 'heat
bath' that surrounds neurons is introduced. The heat bath, which is a source of
noise, is specified by the 'temperature'. Several studies concerning the
pattern-recalling processes of the Hopfield model governed by the
Glauber-dynamics at finite temperature were already reported. However, we might
extend the 'thermal noise' to the quantum-mechanical variant. In this paper, in
terms of the stochastic process of quantum-mechanical Markov chain Monte Carlo
method (the quantum MCMC), we analytically derive macroscopically deterministic
equations of order parameters such as 'overlap' in a quantum-mechanical variant
of the Hopfield neural networks (let us call "quantum Hopfield model" or
"quantum Hopfield networks"). For the case in which non-extensive number $p$ of
patterns are embedded via asymmetric Hebbian connections, namely, $p/N \to 0$
for the number of neuron $N \to \infty$ ('far from saturation'), we evaluate
the recalling processes for one of the built-in patterns under the influence of
quantum-mechanical noise.
| [
"['Jun-ichi Inoue']",
"Jun-ichi Inoue"
] |
q-bio.QM cs.CL cs.IR cs.LG | null | 1103.4090 | null | null | http://arxiv.org/pdf/1103.4090v2 | 2011-04-22T17:46:37Z | 2011-03-21T17:33:32Z | A Linear Classifier Based on Entity Recognition Tools and a Statistical
Approach to Method Extraction in the Protein-Protein Interaction Literature | We participated, in the Article Classification and the Interaction Method
subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of
the BioCreative III Challenge. For the ACT, we pursued an extensive testing of
available Named Entity Recognition and dictionary tools, and used the most
promising ones to extend our Variable Trigonometric Threshold linear
classifier. For the IMT, we experimented with a primarily statistical approach,
as opposed to employing a deeper natural language processing strategy. Finally,
we also studied the benefits of integrating the method extraction approach that
we have used for the IMT into the ACT pipeline. For the ACT, our linear article
classifier leads to a ranking and classification performance significantly
higher than all the reported submissions. For the IMT, our results are
comparable to those of other systems, which took very different approaches. For
the ACT, we show that the use of named entity recognition tools leads to a
substantial improvement in the ranking and classification of articles relevant
to protein-protein interaction. Thus, we show that our substantially expanded
linear classifier is a very competitive classifier in this domain. Moreover,
this classifier produces interpretable surfaces that can be understood as
"rules" for human understanding of the classification. In terms of the IMT
task, in contrast to other participants, our approach focused on identifying
sentences that are likely to bear evidence for the application of a PPI
detection method, rather than on classifying a document as relevant to a
method. As BioCreative III did not perform an evaluation of the evidence
provided by the system, we have conducted a separate assessment; the evaluators
agree that our tool is indeed effective in detecting relevant evidence for PPI
detection methods.
| [
"An\\'alia Louren\\c{c}o, Michael Conover, Andrew Wong, Azadeh\n Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M. Rocha",
"['Anália Lourenço' 'Michael Conover' 'Andrew Wong' 'Azadeh Nematzadeh'\n 'Fengxia Pan' 'Hagit Shatkay' 'Luis M. Rocha']"
] |
cs.LG | null | 1103.4204 | null | null | http://arxiv.org/pdf/1103.4204v1 | 2011-03-22T04:54:35Z | 2011-03-22T04:54:35Z | Parallel Online Learning | In this work we study parallelization of online learning, a core primitive in
machine learning. In a parallel environment all known approaches for parallel
online learning lead to delayed updates, where the model is updated using
out-of-date information. In the worst case, or when examples are temporally
correlated, delay can have a very adverse effect on the learning algorithm.
Here, we analyze and present preliminary empirical results on a set of learning
architectures based on a feature sharding approach that present various
tradeoffs between delay, degree of parallelism, representation power and
empirical performance.
| [
"Daniel Hsu, Nikos Karampatziakis, John Langford, Alex Smola",
"['Daniel Hsu' 'Nikos Karampatziakis' 'John Langford' 'Alex Smola']"
] |
cs.LG stat.ML | null | 1103.4480 | null | null | http://arxiv.org/pdf/1103.4480v1 | 2011-03-23T10:20:14Z | 2011-03-23T10:20:14Z | Clustered regression with unknown clusters | We consider a collection of prediction experiments, which are clustered in
the sense that groups of experiments ex- hibit similar relationship between the
predictor and response variables. The experiment clusters as well as the
regres- sion relationships are unknown. The regression relation- ships define
the experiment clusters, and in general, the predictor and response variables
may not exhibit any clus- tering. We call this prediction problem clustered
regres- sion with unknown clusters (CRUC) and in this paper we focus on linear
regression. We study and compare several methods for CRUC, demonstrate their
applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in-
vestigate an associated mathematical model. CRUC is at the crossroads of many
prior works and we study several prediction algorithms with diverse origins: an
adaptation of the expectation-maximization algorithm, an approach in- spired by
K-means clustering, the singular value threshold- ing approach to matrix rank
minimization under quadratic constraints, an adaptation of the Curds and Whey
method in multiple regression, and a local regression (LoR) scheme reminiscent
of neighborhood methods in collaborative filter- ing. Based on empirical
evaluation on the YLRC dataset as well as simulated data, we identify the LoR
method as a good practical choice: it yields best or near-best prediction
performance at a reasonable computational load, and it is less sensitive to the
choice of the algorithm parameter. We also provide some analysis of the LoR
method for an asso- ciated mathematical model, which sheds light on optimal
parameter choice and prediction performance.
| [
"['Kishor Barman' 'Onkar Dabeer']",
"Kishor Barman, Onkar Dabeer"
] |
cs.LG cs.AI cs.CV cs.NE | null | 1103.4487 | null | null | http://arxiv.org/pdf/1103.4487v1 | 2011-03-23T10:38:50Z | 2011-03-23T10:38:50Z | Handwritten Digit Recognition with a Committee of Deep Neural Nets on
GPUs | The competitive MNIST handwritten digit recognition benchmark has a long
history of broken records since 1998. The most recent substantial improvement
by others dates back 7 years (error rate 0.4%) . Recently we were able to
significantly improve this result, using graphics cards to greatly speed up
training of simple but deep MLPs, which achieved 0.35%, outperforming all the
previous more complex methods. Here we report another substantial improvement:
0.31% obtained using a committee of MLPs.
| [
"Dan C. Cire\\c{s}an, Ueli Meier, Luca M. Gambardella and J\\\"urgen\n Schmidhuber",
"['Dan C. Cireşan' 'Ueli Meier' 'Luca M. Gambardella' 'Jürgen Schmidhuber']"
] |
cs.LG cs.AI cs.RO stat.AP stat.ML | null | 1103.4601 | null | null | http://arxiv.org/pdf/1103.4601v2 | 2011-05-06T02:38:18Z | 2011-03-23T19:37:45Z | Doubly Robust Policy Evaluation and Learning | We study decision making in environments where the reward is only partially
observed, but can be modeled as a function of an action and an observed
context. This setting, known as contextual bandits, encompasses a wide variety
of applications including health-care policy and Internet advertising. A
central task is evaluation of a new policy given historic data consisting of
contexts, actions and received rewards. The key challenge is that the past data
typically does not faithfully represent proportions of actions taken by a new
policy. Previous approaches rely either on models of rewards or models of the
past policy. The former are plagued by a large bias whereas the latter have a
large variance.
In this work, we leverage the strength and overcome the weaknesses of the two
approaches by applying the doubly robust technique to the problems of policy
evaluation and optimization. We prove that this approach yields accurate value
estimates when we have either a good (but not necessarily consistent) model of
rewards or a good (but not necessarily consistent) model of past policy.
Extensive empirical comparison demonstrates that the doubly robust approach
uniformly improves over existing techniques, achieving both lower variance in
value estimation and better policies. As such, we expect the doubly robust
approach to become common practice.
| [
"['Miroslav Dudik' 'John Langford' 'Lihong Li']",
"Miroslav Dudik and John Langford and Lihong Li"
] |
cs.LG stat.ML | null | 1103.4896 | null | null | http://arxiv.org/pdf/1103.4896v1 | 2011-03-25T02:33:27Z | 2011-03-25T02:33:27Z | Classification of Sets using Restricted Boltzmann Machines | We consider the problem of classification when inputs correspond to sets of
vectors. This setting occurs in many problems such as the classification of
pieces of mail containing several pages, of web sites with several sections or
of images that have been pre-segmented into smaller regions. We propose
generalizations of the restricted Boltzmann machine (RBM) that are appropriate
in this context and explore how to incorporate different assumptions about the
relationship between the input sets and the target class within the RBM. In
experiments on standard multiple-instance learning datasets, we demonstrate the
competitiveness of approaches based on RBMs and apply the proposed variants to
the problem of incoming mail classification.
| [
"J\\'er\\^ome Louradour and Hugo Larochelle",
"['Jérôme Louradour' 'Hugo Larochelle']"
] |
cs.LG cs.CC cs.NE | null | 1103.4904 | null | null | http://arxiv.org/pdf/1103.4904v1 | 2011-03-25T04:34:42Z | 2011-03-25T04:34:42Z | Distribution-Independent Evolvability of Linear Threshold Functions | Valiant's (2007) model of evolvability models the evolutionary process of
acquiring useful functionality as a restricted form of learning from random
examples. Linear threshold functions and their various subclasses, such as
conjunctions and decision lists, play a fundamental role in learning theory and
hence their evolvability has been the primary focus of research on Valiant's
framework (2007). One of the main open problems regarding the model is whether
conjunctions are evolvable distribution-independently (Feldman and Valiant,
2008). We show that the answer is negative. Our proof is based on a new
combinatorial parameter of a concept class that lower-bounds the complexity of
learning from correlations.
We contrast the lower bound with a proof that linear threshold functions
having a non-negligible margin on the data points are evolvable
distribution-independently via a simple mutation algorithm. Our algorithm
relies on a non-linear loss function being used to select the hypotheses
instead of 0-1 loss in Valiant's (2007) original definition. The proof of
evolvability requires that the loss function satisfies several mild conditions
that are, for example, satisfied by the quadratic loss function studied in
several other works (Michael, 2007; Feldman, 2009; Valiant, 2010). An important
property of our evolution algorithm is monotonicity, that is the algorithm
guarantees evolvability without any decreases in performance. Previously,
monotone evolvability was only shown for conjunctions with quadratic loss
(Feldman, 2009) or when the distribution on the domain is severely restricted
(Michael, 2007; Feldman, 2009; Kanade et al., 2010)
| [
"['Vitaly Feldman']",
"Vitaly Feldman"
] |
cs.IT cs.LG math.IT | null | 1103.5985 | null | null | http://arxiv.org/pdf/1103.5985v1 | 2011-03-30T16:30:27Z | 2011-03-30T16:30:27Z | On Empirical Entropy | We propose a compression-based version of the empirical entropy of a finite
string over a finite alphabet. Whereas previously one considers the naked
entropy of (possibly higher order) Markov processes, we consider the sum of the
description of the random variable involved plus the entropy it induces. We
assume only that the distribution involved is computable. To test the new
notion we compare the Normalized Information Distance (the similarity metric)
with a related measure based on Mutual Information in Shannon's framework. This
way the similarities and differences of the last two concepts are exposed.
| [
"['Paul M. B. Vitányi']",
"Paul M.B. Vit\\'anyi (CWI and University of Amsterdam)"
] |
cs.LG cs.NI math.PR | null | 1104.0111 | null | null | http://arxiv.org/pdf/1104.0111v1 | 2011-04-01T08:48:54Z | 2011-04-01T08:48:54Z | Decentralized Online Learning Algorithms for Opportunistic Spectrum
Access | The fundamental problem of multiple secondary users contending for
opportunistic spectrum access over multiple channels in cognitive radio
networks has been formulated recently as a decentralized multi-armed bandit
(D-MAB) problem. In a D-MAB problem there are $M$ users and $N$ arms (channels)
that each offer i.i.d. stochastic rewards with unknown means so long as they
are accessed without collision. The goal is to design a decentralized online
learning policy that incurs minimal regret, defined as the difference between
the total expected rewards accumulated by a model-aware genie, and that
obtained by all users applying the policy. We make two contributions in this
paper. First, we consider the setting where the users have a prioritized
ranking, such that it is desired for the $K$-th-ranked user to learn to access
the arm offering the $K$-th highest mean reward. For this problem, we present
the first distributed policy that yields regret that is uniformly logarithmic
over time without requiring any prior assumption about the mean rewards.
Second, we consider the case when a fair access policy is required, i.e., it is
desired for all users to experience the same mean reward. For this problem, we
present a distributed policy that yields order-optimal regret scaling with
respect to the number of users and arms, better than previously proposed
policies in the literature. Both of our distributed policies make use of an
innovative modification of the well known UCB1 policy for the classic
multi-armed bandit problem that allows a single user to learn how to play the
arm that yields the $K$-th largest mean reward.
| [
"['Yi Gai' 'Bhaskar Krishnamachari']",
"Yi Gai and Bhaskar Krishnamachari"
] |
cs.LG | null | 1104.0235 | null | null | http://arxiv.org/pdf/1104.0235v1 | 2011-04-01T19:33:05Z | 2011-04-01T19:33:05Z | Gaussian Robust Classification | Supervised learning is all about the ability to generalize knowledge.
Specifically, the goal of the learning is to train a classifier using training
data, in such a way that it will be capable of classifying new unseen data
correctly. In order to acheive this goal, it is important to carefully design
the learner, so it will not overfit the training data. The later can is done
usually by adding a regularization term. The statistical learning theory
explains the success of this method by claiming that it restricts the
complexity of the learned model. This explanation, however, is rather abstract
and does not have a geometric intuition. The generalization error of a
classifier may be thought of as correlated with its robustness to perturbations
of the data: a classifier that copes with disturbance is expected to generalize
well. Indeed, Xu et al. [2009] have shown that the SVM formulation is
equivalent to a robust optimization (RO) formulation, in which an adversary
displaces the training and testing points within a ball of pre-determined
radius. In this work we explore a different kind of robustness, namely changing
each data point with a Gaussian cloud centered at the sample. Loss is evaluated
as the expectation of an underlying loss function on the cloud. This setup fits
the fact that in many applications, the data is sampled along with noise. We
develop an RO framework, in which the adversary chooses the covariance of the
noise. In our algorithm named GURU, the tuning parameter is a spectral bound on
the noise, thus it can be estimated using physical or applicative
considerations. Our experiments show that this framework performs as well as
SVM and even slightly better in some cases. Generalizations for Mercer kernels
and for the multiclass case are presented as well. We also show that our
framework may be further generalized, using the technique of convex perspective
functions.
| [
"['Ido Ginodi' 'Amir Globerson']",
"Ido Ginodi, Amir Globerson"
] |
cs.LG | null | 1104.0651 | null | null | http://arxiv.org/pdf/1104.0651v3 | 2011-07-19T14:39:35Z | 2011-04-04T19:04:25Z | Meaningful Clustered Forest: an Automatic and Robust Clustering
Algorithm | We propose a new clustering technique that can be regarded as a numerical
method to compute the proximity gestalt. The method analyzes edge length
statistics in the MST of the dataset and provides an a contrario cluster
detection criterion. The approach is fully parametric on the chosen distance
and can detect arbitrarily shaped clusters. The method is also automatic, in
the sense that only a single parameter is left to the user. This parameter has
an intuitive interpretation as it controls the expected number of false
detections. We show that the iterative application of our method can (1)
provide robustness to noise and (2) solve a masking phenomenon in which a
highly populated and salient cluster dominates the scene and inhibits the
detection of less-populated, but still salient, clusters.
| [
"['Mariano Tepper' 'Pablo Musé' 'Andrés Almansa']",
"Mariano Tepper, Pablo Mus\\'e, Andr\\'es Almansa"
] |
cs.LG stat.ML | null | 1104.0729 | null | null | http://arxiv.org/pdf/1104.0729v4 | 2011-06-16T15:40:28Z | 2011-04-05T04:28:51Z | Online and Batch Learning Algorithms for Data with Missing Features | We introduce new online and batch algorithms that are robust to data with
missing features, a situation that arises in many practical applications. In
the online setup, we allow for the comparison hypothesis to change as a
function of the subset of features that is observed on any given round,
extending the standard setting where the comparison hypothesis is fixed
throughout. In the batch setup, we present a convex relation of a non-convex
problem to jointly estimate an imputation function, used to fill in the values
of missing features, along with the classification hypothesis. We prove regret
bounds in the online setting and Rademacher complexity bounds for the batch
i.i.d. setting. The algorithms are tested on several UCI datasets, showing
superior performance over baselines.
| [
"['Afshin Rostamizadeh' 'Alekh Agarwal' 'Peter Bartlett']",
"Afshin Rostamizadeh, Alekh Agarwal, Peter Bartlett"
] |
cs.LG math.OC stat.ME stat.ML | null | 1104.1436 | null | null | http://arxiv.org/pdf/1104.1436v1 | 2011-04-07T20:05:48Z | 2011-04-07T20:05:48Z | Efficient First Order Methods for Linear Composite Regularizers | A wide class of regularization problems in machine learning and statistics
employ a regularization term which is obtained by composing a simple convex
function \omega with a linear transformation. This setting includes Group Lasso
methods, the Fused Lasso and other total variation methods, multi-task learning
methods and many more. In this paper, we present a general approach for
computing the proximity operator of this class of regularizers, under the
assumption that the proximity operator of the function \omega is known in
advance. Our approach builds on a recent line of research on optimal first
order optimization methods and uses fixed point iterations for numerically
computing the proximity operator. It is more general than current approaches
and, as we show with numerical simulations, computationally more efficient than
available first order methods which do not achieve the optimal rate. In
particular, our method outperforms state of the art O(1/T) methods for
overlapping Group Lasso and matches optimal O(1/T^2) methods for the Fused
Lasso and tree structured Group Lasso.
| [
"Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Lixin\n Shen, Yuesheng Xu",
"['Andreas Argyriou' 'Charles A. Micchelli' 'Massimiliano Pontil'\n 'Lixin Shen' 'Yuesheng Xu']"
] |
math.ST cs.LG stat.TH | null | 1104.1450 | null | null | http://arxiv.org/pdf/1104.1450v2 | 2011-11-02T03:20:06Z | 2011-04-07T21:54:09Z | Plug-in Approach to Active Learning | We present a new active learning algorithm based on nonparametric estimators
of the regression function. Our investigation provides probabilistic bounds for
the rates of convergence of the generalization error achievable by proposed
method over a broad class of underlying distributions. We also prove minimax
lower bounds which show that the obtained rates are almost tight.
| [
"Stanislav Minsker",
"['Stanislav Minsker']"
] |
math.PR cs.LG stat.ML | null | 1104.1672 | null | null | http://arxiv.org/pdf/1104.1672v3 | 2011-04-16T04:17:23Z | 2011-04-09T04:25:04Z | Dimension-free tail inequalities for sums of random matrices | We derive exponential tail inequalities for sums of random matrices with no
dependence on the explicit matrix dimensions. These are similar to the matrix
versions of the Chernoff bound and Bernstein inequality except with the
explicit matrix dimensions replaced by a trace quantity that can be small even
when the dimension is large or infinite. Some applications to principal
component analysis and approximate matrix multiplication are given to
illustrate the utility of the new bounds.
| [
"['Daniel Hsu' 'Sham M. Kakade' 'Tong Zhang']",
"Daniel Hsu, Sham M. Kakade, Tong Zhang"
] |
math.OC cs.LG stat.ML | null | 1104.1872 | null | null | http://arxiv.org/pdf/1104.1872v3 | 2011-09-16T05:26:00Z | 2011-04-11T08:04:59Z | Convex and Network Flow Optimization for Structured Sparsity | We consider a class of learning problems regularized by a structured
sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over
groups of variables. Whereas much effort has been put in developing fast
optimization techniques when the groups are disjoint or embedded in a
hierarchy, we address here the case of general overlapping groups. To this end,
we present two different strategies: On the one hand, we show that the proximal
operator associated with a sum of l_infinity-norms can be computed exactly in
polynomial time by solving a quadratic min-cost flow problem, allowing the use
of accelerated proximal gradient methods. On the other hand, we use proximal
splitting techniques, and address an equivalent formulation with
non-overlapping groups, but in higher dimension and with additional
constraints. We propose efficient and scalable algorithms exploiting these two
strategies, which are significantly faster than alternative approaches. We
illustrate these methods with several problems such as CUR matrix
factorization, multi-task learning of tree-structured dictionaries, background
subtraction in video sequences, image denoising with wavelets, and topographic
dictionary learning of natural image patches.
| [
"Julien Mairal, Rodolphe Jenatton (LIENS, INRIA Paris - Rocquencourt),\n Guillaume Obozinski (LIENS, INRIA Paris - Rocquencourt), Francis Bach (LIENS,\n INRIA Paris - Rocquencourt)",
"['Julien Mairal' 'Rodolphe Jenatton' 'Guillaume Obozinski' 'Francis Bach']"
] |
cs.LG stat.ML | 10.1007/s10618-012-0302-x | 1104.1990 | null | null | http://arxiv.org/abs/1104.1990v3 | 2013-02-19T16:17:49Z | 2011-04-11T16:38:50Z | Adaptive Evolutionary Clustering | In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.
| [
"Kevin S. Xu, Mark Kliger, Alfred O. Hero III",
"['Kevin S. Xu' 'Mark Kliger' 'Alfred O. Hero III']"
] |
cs.AI cs.LG stat.ML | null | 1104.2018 | null | null | http://arxiv.org/pdf/1104.2018v1 | 2011-04-11T18:24:01Z | 2011-04-11T18:24:01Z | Efficient Learning of Generalized Linear and Single Index Models with
Isotonic Regression | Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide
powerful generalizations of linear regression, where the target variable is
assumed to be a (possibly unknown) 1-dimensional function of a linear
predictor. In general, these problems entail non-convex estimation procedures,
and, in practice, iterative local search heuristics are often used. Kalai and
Sastry (2009) recently provided the first provably efficient method for
learning SIMs and GLMs, under the assumptions that the data are in fact
generated under a GLM and under certain monotonicity and Lipschitz constraints.
However, to obtain provable performance, the method requires a fresh sample
every iteration. In this paper, we provide algorithms for learning GLMs and
SIMs, which are both computationally and statistically efficient. We also
provide an empirical study, demonstrating their feasibility in practice.
| [
"['Sham Kakade' 'Adam Tauman Kalai' 'Varun Kanade' 'Ohad Shamir']",
"Sham Kakade and Adam Tauman Kalai and Varun Kanade and Ohad Shamir"
] |
cs.LG | null | 1104.2097 | null | null | http://arxiv.org/pdf/1104.2097v1 | 2011-04-12T01:15:03Z | 2011-04-12T01:15:03Z | PAC learnability versus VC dimension: a footnote to a basic result of
statistical learning | A fundamental result of statistical learnig theory states that a concept
class is PAC learnable if and only if it is a uniform Glivenko-Cantelli class
if and only if the VC dimension of the class is finite. However, the theorem is
only valid under special assumptions of measurability of the class, in which
case the PAC learnability even becomes consistent. Otherwise, there is a
classical example, constructed under the Continuum Hypothesis by Dudley and
Durst and further adapted by Blumer, Ehrenfeucht, Haussler, and Warmuth, of a
concept class of VC dimension one which is neither uniform Glivenko-Cantelli
nor consistently PAC learnable. We show that, rather surprisingly, under an
additional set-theoretic hypothesis which is much milder than the Continuum
Hypothesis (Martin's Axiom), PAC learnability is equivalent to finite VC
dimension for every concept class.
| [
"['Vladimir Pestov']",
"Vladimir Pestov"
] |
cs.CV cs.CG cs.GR cs.LG | null | 1104.2580 | null | null | http://arxiv.org/pdf/1104.2580v2 | 2011-08-15T19:31:24Z | 2011-04-13T18:59:52Z | Hypothesize and Bound: A Computational Focus of Attention Mechanism for
Simultaneous N-D Segmentation, Pose Estimation and Classification Using Shape
Priors | Given the ever increasing bandwidth of the visual information available to
many intelligent systems, it is becoming essential to endow them with a sense
of what is worthwhile their attention and what can be safely disregarded. This
article presents a general mathematical framework to efficiently allocate the
available computational resources to process the parts of the input that are
relevant to solve a given perceptual problem. By this we mean to find the
hypothesis H (i.e., the state of the world) that maximizes a function L(H),
representing how well each hypothesis "explains" the input. Given the large
bandwidth of the sensory input, fully evaluating L(H) for each hypothesis H is
computationally infeasible (e.g., because it would imply checking a large
number of pixels). To address this problem we propose a mathematical framework
with two key ingredients. The first one is a Bounding Mechanism (BM) to compute
lower and upper bounds of L(H), for a given computational budget. These bounds
are much cheaper to compute than L(H) itself, can be refined at any time by
increasing the budget allocated to a hypothesis, and are frequently enough to
discard a hypothesis. To compute these bounds, we develop a novel theory of
shapes and shape priors. The second ingredient is a Focus of Attention
Mechanism (FoAM) to select which hypothesis' bounds should be refined next,
with the goal of discarding non-optimal hypotheses with the least amount of
computation. The proposed framework: 1) is very efficient since most hypotheses
are discarded with minimal computation; 2) is parallelizable; 3) is guaranteed
to find the globally optimal hypothesis; and 4) its running time depends on the
problem at hand, not on the bandwidth of the input. We instantiate the proposed
framework for the problem of simultaneously estimating the class, pose, and a
noiseless version of a 2D shape in a 2D image.
| [
"['Diego Rother' 'Simon Schütz' 'René Vidal']",
"Diego Rother, Simon Sch\\\"utz, Ren\\'e Vidal"
] |
stat.ME cs.LG stat.ML | 10.1016/j.csda.2013.04.010 | 1104.2930 | null | null | http://arxiv.org/abs/1104.2930v3 | 2013-05-23T21:17:26Z | 2011-04-14T21:29:10Z | Cluster Forests | With inspiration from Random Forests (RF) in the context of classification, a
new clustering ensemble method---Cluster Forests (CF) is proposed.
Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good
local clusterings" and then aggregates via spectral clustering to obtain
cluster assignments for the whole dataset. The search for good local
clusterings is guided by a cluster quality measure kappa. CF progressively
improves each local clustering in a fashion that resembles the tree growth in
RF. Empirical studies on several real-world datasets under two different
performance metrics show that CF compares favorably to its competitors.
Theoretical analysis reveals that the kappa measure makes it possible to grow
the local clustering in a desirable way---it is "noise-resistant". A
closed-form expression is obtained for the mis-clustering rate of spectral
clustering under a perturbation model, which yields new insights into some
aspects of spectral clustering.
| [
"Donghui Yan, Aiyou Chen, Michael I. Jordan",
"['Donghui Yan' 'Aiyou Chen' 'Michael I. Jordan']"
] |
astro-ph.IM cs.LG physics.data-an | 10.1016/j.nima.2010.11.016 | 1104.3248 | null | null | http://arxiv.org/abs/1104.3248v1 | 2011-04-16T17:35:20Z | 2011-04-16T17:35:20Z | Signal Classification for Acoustic Neutrino Detection | This article focuses on signal classification for deep-sea acoustic neutrino
detection. In the deep sea, the background of transient signals is very
diverse. Approaches like matched filtering are not sufficient to distinguish
between neutrino-like signals and other transient signals with similar
signature, which are forming the acoustic background for neutrino detection in
the deep-sea environment. A classification system based on machine learning
algorithms is analysed with the goal to find a robust and effective way to
perform this task. For a well-trained model, a testing error on the level of
one percent is achieved for strong classifiers like Random Forest and Boosting
Trees using the extracted features of the signal as input and utilising dense
clusters of sensors instead of single sensors.
| [
"['M. Neff' 'G. Anton' 'A. Enzenhöfer' 'K. Graf' 'J. Hößl' 'U. Katz'\n 'R. Lahmann' 'C. Richardt']",
"M. Neff, G. Anton, A. Enzenh\\\"ofer, K. Graf, J. H\\\"o{\\ss}l, U. Katz,\n R. Lahmann and C. Richardt"
] |
stat.ML cs.LG math.NA | null | 1104.3792 | null | null | http://arxiv.org/pdf/1104.3792v1 | 2011-04-19T16:19:03Z | 2011-04-19T16:19:03Z | A sufficient condition on monotonic increase of the number of nonzero
entry in the optimizer of L1 norm penalized least-square problem | The $\ell$-1 norm based optimization is widely used in signal processing,
especially in recent compressed sensing theory. This paper studies the solution
path of the $\ell$-1 norm penalized least-square problem, whose constrained
form is known as Least Absolute Shrinkage and Selection Operator (LASSO). A
solution path is the set of all the optimizers with respect to the evolution of
the hyperparameter (Lagrange multiplier). The study of the solution path is of
great significance in viewing and understanding the profile of the tradeoff
between the approximation and regularization terms. If the solution path of a
given problem is known, it can help us to find the optimal hyperparameter under
a given criterion such as the Akaike Information Criterion. In this paper we
present a sufficient condition on $\ell$-1 norm penalized least-square problem.
Under this sufficient condition, the number of nonzero entries in the optimizer
or solution vector increases monotonically when the hyperparameter decreases.
We also generalize the result to the often used total variation case, where the
$\ell$-1 norm is taken over the first order derivative of the solution vector.
We prove that the proposed condition has intrinsic connections with the
condition given by Donoho, et al \cite{Donoho08} and the positive cone
condition by Efron {\it el al} \cite{Efron04}. However, the proposed condition
does not need to assume the sparsity level of the signal as required by Donoho
et al's condition, and is easier to verify than Efron, et al's positive cone
condition when being used for practical applications.
| [
"J. Duan, Charles Soussen, David Brie, Jerome Idier and Y.-P. Wang",
"['J. Duan' 'Charles Soussen' 'David Brie' 'Jerome Idier' 'Y. -P. Wang']"
] |
cs.AI cs.LG | null | 1104.3929 | null | null | http://arxiv.org/pdf/1104.3929v1 | 2011-04-20T02:49:59Z | 2011-04-20T02:49:59Z | Understanding Exhaustive Pattern Learning | Pattern learning in an important problem in Natural Language Processing
(NLP). Some exhaustive pattern learning (EPL) methods (Bod, 1992) were proved
to be flawed (Johnson, 2002), while similar algorithms (Och and Ney, 2004)
showed great advantages on other tasks, such as machine translation. In this
article, we first formalize EPL, and then show that the probability given by an
EPL model is constant-factor approximation of the probability given by an
ensemble method that integrates exponential number of models obtained with
various segmentations of the training data. This work for the first time
provides theoretical justification for the widely used EPL algorithm in NLP,
which was previously viewed as a flawed heuristic method. Better understanding
of EPL may lead to improved pattern learning algorithms in future.
| [
"Libin Shen",
"['Libin Shen']"
] |
stat.ME cs.CV cs.LG | null | 1104.4376 | null | null | http://arxiv.org/pdf/1104.4376v1 | 2011-04-22T02:27:45Z | 2011-04-22T02:27:45Z | Intent Inference and Syntactic Tracking with GMTI Measurements | In conventional target tracking systems, human operators use the estimated
target tracks to make higher level inference of the target behaviour/intent.
This paper develops syntactic filtering algorithms that assist human operators
by extracting spatial patterns from target tracks to identify
suspicious/anomalous spatial trajectories. The targets' spatial trajectories
are modeled by a stochastic context free grammar (SCFG) and a switched mode
state space model. Bayesian filtering algorithms for stochastic context free
grammars are presented for extracting the syntactic structure and illustrated
for a ground moving target indicator (GMTI) radar example. The performance of
the algorithms is tested with the experimental data collected using DRDC
Ottawa's X-band Wideband Experimental Airborne Radar (XWEAR).
| [
"Alex Wang and Vikram Krishnamurthy and Bhashyam Balaji",
"['Alex Wang' 'Vikram Krishnamurthy' 'Bhashyam Balaji']"
] |
stat.ML cs.LG | 10.1109/TSP.2012.2196696 | 1104.4512 | null | null | http://arxiv.org/abs/1104.4512v1 | 2011-04-22T22:01:14Z | 2011-04-22T22:01:14Z | Robust Clustering Using Outlier-Sparsity Regularization | Notwithstanding the popularity of conventional clustering algorithms such as
K-means and probabilistic clustering, their clustering results are sensitive to
the presence of outliers in the data. Even a few outliers can compromise the
ability of these algorithms to identify meaningful hidden structures rendering
their outcome unreliable. This paper develops robust clustering algorithms that
not only aim to cluster the data, but also to identify the outliers. The novel
approaches rely on the infrequent presence of outliers in the data which
translates to sparsity in a judiciously chosen domain. Capitalizing on the
sparsity in the outlier domain, outlier-aware robust K-means and probabilistic
clustering approaches are proposed. Their novelty lies on identifying outliers
while effecting sparsity in the outlier domain through carefully chosen
regularization. A block coordinate descent approach is developed to obtain
iterative algorithms with convergence guarantees and small excess computational
complexity with respect to their non-robust counterparts. Kernelized versions
of the robust clustering algorithms are also developed to efficiently handle
high-dimensional data, identify nonlinearly separable clusters, or even cluster
objects that are not represented by vectors. Numerical tests on both synthetic
and real datasets validate the performance and applicability of the novel
algorithms.
| [
"Pedro A. Forero, Vassilis Kekatos, Georgios B. Giannakis",
"['Pedro A. Forero' 'Vassilis Kekatos' 'Georgios B. Giannakis']"
] |
stat.ML cs.DM cs.LG cs.SI physics.soc-ph | null | 1104.4605 | null | null | http://arxiv.org/pdf/1104.4605v1 | 2011-04-24T06:06:12Z | 2011-04-24T06:06:12Z | Compressive Network Analysis | Modern data acquisition routinely produces massive amounts of network data.
Though many methods and models have been proposed to analyze such data, the
research of network data is largely disconnected with the classical theory of
statistical learning and signal processing. In this paper, we present a new
framework for modeling network data, which connects two seemingly different
areas: network data analysis and compressed sensing. From a nonparametric
perspective, we model an observed network using a large dictionary. In
particular, we consider the network clique detection problem and show
connections between our formulation with a new algebraic tool, namely Randon
basis pursuit in homogeneous spaces. Such a connection allows us to identify
rigorous recovery conditions for clique detection problems. Though this paper
is mainly conceptual, we also develop practical approximation algorithms for
solving empirical problems and demonstrate their usefulness on real-world
datasets.
| [
"Xiaoye Jiang and Yuan Yao and Han Liu and Leonidas Guibas",
"['Xiaoye Jiang' 'Yuan Yao' 'Han Liu' 'Leonidas Guibas']"
] |
Subsets and Splits