categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
cs.LG cs.AI | 10.1016/j.bspc.2013.06.004 | 1212.2262 | null | null | http://arxiv.org/abs/1212.2262v1 | 2012-12-11T00:49:27Z | 2012-12-11T00:49:27Z | Bag-of-Words Representation for Biomedical Time Series Classification | Automatic analysis of biomedical time series such as electroencephalogram
(EEG) and electrocardiographic (ECG) signals has attracted great interest in
the community of biomedical engineering due to its important applications in
medicine. In this work, a simple yet effective bag-of-words representation that
is able to capture both local and global structure similarity information is
proposed for biomedical time series representation. In particular, similar to
the bag-of-words model used in text document domain, the proposed method treats
a time series as a text document and extracts local segments from the time
series as words. The biomedical time series is then represented as a histogram
of codewords, each entry of which is the count of a codeword appeared in the
time series. Although the temporal order of the local segments is ignored, the
bag-of-words representation is able to capture high-level structural
information because both local and global structural information are well
utilized. The performance of the bag-of-words model is validated on three
datasets extracted from real EEG and ECG signals. The experimental results
demonstrate that the proposed method is not only insensitive to parameters of
the bag-of-words model such as local segment length and codebook size, but also
robust to noise.
| [
"Jin Wang, Ping Liu, Mary F.H.She, Saeid Nahavandi and and Abbas\n Kouzani",
"['Jin Wang' 'Ping Liu' 'Mary F. H. She' 'Saeid Nahavandi'\n 'and Abbas Kouzani']"
] |
cs.DB cs.IR cs.LG | null | 1212.2287 | null | null | http://arxiv.org/pdf/1212.2287v2 | 2013-04-26T16:33:08Z | 2012-12-11T03:20:46Z | Runtime Optimizations for Prediction with Tree-Based Models | Tree-based models have proven to be an effective solution for web ranking as
well as other problems in diverse domains. This paper focuses on optimizing the
runtime performance of applying such models to make predictions, given an
already-trained model. Although exceedingly simple conceptually, most
implementations of tree-based models do not efficiently utilize modern
superscalar processor architectures. By laying out data structures in memory in
a more cache-conscious fashion, removing branches from the execution flow using
a technique called predication, and micro-batching predictions using a
technique called vectorization, we are able to better exploit modern processor
architectures and significantly improve the speed of tree-based models over
hard-coded if-else blocks. Our work contributes to the exploration of
architecture-conscious runtime implementations of machine learning algorithms.
| [
"Nima Asadi, Jimmy Lin, and Arjen P. de Vries",
"['Nima Asadi' 'Jimmy Lin' 'Arjen P. de Vries']"
] |
stat.ML cs.LG | null | 1212.2340 | null | null | http://arxiv.org/pdf/1212.2340v1 | 2012-12-11T09:03:17Z | 2012-12-11T09:03:17Z | PAC-Bayesian Learning and Domain Adaptation | In machine learning, Domain Adaptation (DA) arises when the distribution gen-
erating the test (target) data differs from the one generating the learning
(source) data. It is well known that DA is an hard task even under strong
assumptions, among which the covariate-shift where the source and target
distributions diverge only in their marginals, i.e. they have the same labeling
function. Another popular approach is to consider an hypothesis class that
moves closer the two distributions while implying a low-error for both tasks.
This is a VC-dim approach that restricts the complexity of an hypothesis class
in order to get good generalization. Instead, we propose a PAC-Bayesian
approach that seeks for suitable weights to be given to each hypothesis in
order to build a majority vote. We prove a new DA bound in the PAC-Bayesian
context. This leads us to design the first DA-PAC-Bayesian algorithm based on
the minimization of the proposed bound. Doing so, we seek for a \rho-weighted
majority vote that takes into account a trade-off between three quantities. The
first two quantities being, as usual in the PAC-Bayesian approach, (a) the
complexity of the majority vote (measured by a Kullback-Leibler divergence) and
(b) its empirical risk (measured by the \rho-average errors on the source
sample). The third quantity is (c) the capacity of the majority vote to
distinguish some structural difference between the source and target samples.
| [
"['Pascal Germain' 'Amaury Habrard' 'François Laviolette' 'Emilie Morvant']",
"Pascal Germain, Amaury Habrard (LAHC), Fran\\c{c}ois Laviolette, Emilie\n Morvant (LIF)"
] |
cs.CL cs.LG | null | 1212.2390 | null | null | http://arxiv.org/pdf/1212.2390v1 | 2012-12-11T11:35:30Z | 2012-12-11T11:35:30Z | On the complexity of learning a language: An improvement of Block's
algorithm | Language learning is thought to be a highly complex process. One of the
hurdles in learning a language is to learn the rules of syntax of the language.
Rules of syntax are often ordered in that before one rule can applied one must
apply another. It has been thought that to learn the order of n rules one must
go through all n! permutations. Thus to learn the order of 27 rules would
require 27! steps or 1.08889x10^{28} steps. This number is much greater than
the number of seconds since the beginning of the universe! In an insightful
analysis the linguist Block ([Block 86], pp. 62-63, p.238) showed that with the
assumption of transitivity this vast number of learning steps reduces to a mere
377 steps. We present a mathematical analysis of the complexity of Block's
algorithm. The algorithm has a complexity of order n^2 given n rules. In
addition, we improve Block's results exponentially, by introducing an algorithm
that has complexity of order less than n log n.
| [
"Eric Werner",
"['Eric Werner']"
] |
cs.CR cs.LG | 10.5121/ijnsa | 1212.2414 | null | null | http://arxiv.org/abs/1212.2414v1 | 2012-12-11T13:14:42Z | 2012-12-11T13:14:42Z | Mining Techniques in Network Security to Enhance Intrusion Detection
Systems | In intrusion detection systems, classifiers still suffer from several
drawbacks such as data dimensionality and dominance, different network feature
types, and data impact on the classification. In this paper two significant
enhancements are presented to solve these drawbacks. The first enhancement is
an improved feature selection using sequential backward search and information
gain. This, in turn, extracts valuable features that enhance positively the
detection rate and reduce the false positive rate. The second enhancement is
transferring nominal network features to numeric ones by exploiting the
discrete random variable and the probability mass function to solve the problem
of different feature types, the problem of data dominance, and data impact on
the classification. The latter is combined to known normalization methods to
achieve a significant hybrid normalization approach. Finally, an intensive and
comparative study approves the efficiency of these enhancements and shows
better performance comparing to other proposed methods.
| [
"['Maher Salem' 'Ulrich Buehler']",
"Maher Salem and Ulrich Buehler"
] |
cs.LG cs.CV | null | 1212.2415 | null | null | http://arxiv.org/pdf/1212.2415v1 | 2012-12-11T13:19:54Z | 2012-12-11T13:19:54Z | Robust Face Recognition using Local Illumination Normalization and
Discriminant Feature Point Selection | Face recognition systems must be robust to the variation of various factors
such as facial expression, illumination, head pose and aging. Especially, the
robustness against illumination variation is one of the most important problems
to be solved for the practical use of face recognition systems. Gabor wavelet
is widely used in face detection and recognition because it gives the
possibility to simulate the function of human visual system. In this paper, we
propose a method for extracting Gabor wavelet features which is stable under
the variation of local illumination and show experiment results demonstrating
its effectiveness.
| [
"['Song Han' 'Jinsong Kim' 'Cholhun Kim' 'Jongchol Jo' 'Sunam Han']",
"Song Han, Jinsong Kim, Cholhun Kim, Jongchol Jo, and Sunam Han"
] |
cs.IR cs.LG stat.ML | null | 1212.2442 | null | null | http://arxiv.org/pdf/1212.2442v1 | 2012-10-19T15:04:12Z | 2012-10-19T15:04:12Z | Active Collaborative Filtering | Collaborative filtering (CF) allows the preferences of multiple users to be
pooled to make recommendations regarding unseen products. We consider in this
paper the problem of online and interactive CF: given the current ratings
associated with a user, what queries (new ratings) would most improve the
quality of the recommendations made? We cast this terms of expected value of
information (EVOI); but the online computational cost of computing optimal
queries is prohibitive. We show how offline prototyping and computation of
bounds on EVOI can be used to dramatically reduce the required online
computation. The framework we develop is general, but we focus on derivations
and empirical study in the specific case of the multiple-cause vector
quantization model.
| [
"Craig Boutilier, Richard S. Zemel, Benjamin Marlin",
"['Craig Boutilier' 'Richard S. Zemel' 'Benjamin Marlin']"
] |
cs.LG stat.ML | null | 1212.2447 | null | null | http://arxiv.org/pdf/1212.2447v1 | 2012-10-19T15:03:51Z | 2012-10-19T15:03:51Z | Bayesian Hierarchical Mixtures of Experts | The Hierarchical Mixture of Experts (HME) is a well-known tree-based model
for regression and classification, based on soft probabilistic splits. In its
original formulation it was trained by maximum likelihood, and is therefore
prone to over-fitting. Furthermore the maximum likelihood framework offers no
natural metric for optimizing the complexity and structure of the tree.
Previous attempts to provide a Bayesian treatment of the HME model have relied
either on ad-hoc local Gaussian approximations or have dealt with related
models representing the joint distribution of both input and output variables.
In this paper we describe a fully Bayesian treatment of the HME model based on
variational inference. By combining local and global variational methods we
obtain a rigourous lower bound on the marginal probability of the data under
the model. This bound is optimized during the training phase, and its resulting
value can be used for model order selection. We present results using this
approach for a data set describing robot arm kinematics.
| [
"Christopher M. Bishop, Markus Svensen",
"['Christopher M. Bishop' 'Markus Svensen']"
] |
cs.LG stat.ML | null | 1212.2460 | null | null | http://arxiv.org/pdf/1212.2460v1 | 2012-10-19T15:05:02Z | 2012-10-19T15:05:02Z | The Information Bottleneck EM Algorithm | Learning with hidden variables is a central challenge in probabilistic
graphical models that has important implications for many real-life problems.
The classical approach is using the Expectation Maximization (EM) algorithm.
This algorithm, however, can get trapped in local maxima. In this paper we
explore a new approach that is based on the Information Bottleneck principle.
In this approach, we view the learning problem as a tradeoff between two
information theoretic objectives. The first is to make the hidden variables
uninformative about the identity of specific instances. The second is to make
the hidden variables informative about the observed attributes. By exploring
different tradeoffs between these two objectives, we can gradually converge on
a high-scoring solution. As we show, the resulting, Information Bottleneck
Expectation Maximization (IB-EM) algorithm, manages to find solutions that are
superior to standard EM methods.
| [
"['Gal Elidan' 'Nir Friedman']",
"Gal Elidan, Nir Friedman"
] |
stat.ME cs.LG stat.ML | null | 1212.2462 | null | null | http://arxiv.org/pdf/1212.2462v1 | 2012-10-19T15:04:52Z | 2012-10-19T15:04:52Z | A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical
Models for Marginal Independence | Graphical models with bi-directed edges (<->) represent marginal
independence: the absence of an edge between two vertices indicates that the
corresponding variables are marginally independent. In this paper, we consider
maximum likelihood estimation in the case of continuous variables with a
Gaussian joint distribution, sometimes termed a covariance graph model. We
present a new fitting algorithm which exploits standard regression techniques
and establish its convergence properties. Moreover, we contrast our procedure
to existing estimation methods.
| [
"['Mathias Drton' 'Thomas S. Richardson']",
"Mathias Drton, Thomas S. Richardson"
] |
cs.AI cs.LG stat.ML | null | 1212.2464 | null | null | http://arxiv.org/pdf/1212.2464v1 | 2012-10-19T15:04:44Z | 2012-10-19T15:04:44Z | A Robust Independence Test for Constraint-Based Learning of Causal
Structure | Constraint-based (CB) learning is a formalism for learning a causal network
with a database D by performing a series of conditional-independence tests to
infer structural information. This paper considers a new test of independence
that combines ideas from Bayesian learning, Bayesian network inference, and
classical hypothesis testing to produce a more reliable and robust test. The
new test can be calculated in the same asymptotic time and space required for
the standard tests such as the chi-squared test, but it allows the
specification of a prior distribution over parameters and can be used when the
database is incomplete. We prove that the test is correct, and we demonstrate
empirically that, when used with a CB causal discovery algorithm with
noninformative priors, it recovers structural features more reliably and it
produces networks with smaller KL-Divergence, especially as the number of nodes
increases or the number of records decreases. Another benefit is the dramatic
reduction in the probability that a CB algorithm will stall during the search,
providing a remedy for an annoying problem plaguing CB learning when the
database is small.
| [
"['Denver Dash' 'Marek J. Druzdzel']",
"Denver Dash, Marek J. Druzdzel"
] |
cs.LG stat.ML | null | 1212.2466 | null | null | http://arxiv.org/pdf/1212.2466v1 | 2012-10-19T15:04:36Z | 2012-10-19T15:04:36Z | On Information Regularization | We formulate a principle for classification with the knowledge of the
marginal distribution over the data points (unlabeled data). The principle is
cast in terms of Tikhonov style regularization where the regularization penalty
articulates the way in which the marginal density should constrain otherwise
unrestricted conditional distributions. Specifically, the regularization
penalty penalizes any information introduced between the examples and labels
beyond what is provided by the available labeled examples. The work extends
Szummer and Jaakkola's information regularization (NIPS 2002) to multiple
dimensions, providing a regularizer independent of the covering of the space
used in the derivation. We show in addition how the information regularizer can
be used as a measure of complexity of the classification task with unlabeled
data and prove a relevant sample-complexity bound. We illustrate the
regularization principle in practice by restricting the class of conditional
distributions to be logistic regression models and constructing the
regularization penalty from a finite set of unlabeled examples.
| [
"['Adrian Corduneanu' 'Tommi S. Jaakkola']",
"Adrian Corduneanu, Tommi S. Jaakkola"
] |
cs.LG cs.AI stat.ML | null | 1212.2468 | null | null | http://arxiv.org/pdf/1212.2468v1 | 2012-10-19T15:04:28Z | 2012-10-19T15:04:28Z | Large-Sample Learning of Bayesian Networks is NP-Hard | In this paper, we provide new complexity results for algorithms that learn
discrete-variable Bayesian networks from data. Our results apply whenever the
learning algorithm uses a scoring criterion that favors the simplest model able
to represent the generative distribution exactly. Our results therefore hold
whenever the learning algorithm uses a consistent scoring criterion and is
applied to a sufficiently large dataset. We show that identifying high-scoring
structures is hard, even when we are given an independence oracle, an inference
oracle, and/or an information oracle. Our negative results also apply to the
learning of discrete-variable Bayesian networks in which each node has at most
k parents, for all k > 3.
| [
"David Maxwell Chickering, Christopher Meek, David Heckerman",
"['David Maxwell Chickering' 'Christopher Meek' 'David Heckerman']"
] |
cs.LG cs.AI stat.ML | null | 1212.2470 | null | null | http://arxiv.org/pdf/1212.2470v1 | 2012-10-19T15:04:17Z | 2012-10-19T15:04:17Z | Reasoning about Bayesian Network Classifiers | Bayesian network classifiers are used in many fields, and one common class of
classifiers are naive Bayes classifiers. In this paper, we introduce an
approach for reasoning about Bayesian network classifiers in which we
explicitly convert them into Ordered Decision Diagrams (ODDs), which are then
used to reason about the properties of these classifiers. Specifically, we
present an algorithm for converting any naive Bayes classifier into an ODD, and
we show theoretically and experimentally that this algorithm can give us an ODD
that is tractable in size even given an intractable number of instances. Since
ODDs are tractable representations of classifiers, our algorithm allows us to
efficiently test the equivalence of two naive Bayes classifiers and
characterize discrepancies between them. We also show a number of additional
results including a count of distinct classifiers that can be induced by
changing some CPT in a naive Bayes classifier, and the range of allowable
changes to a CPT which keeps the current classifier unchanged.
| [
"Hei Chan, Adnan Darwiche",
"['Hei Chan' 'Adnan Darwiche']"
] |
cs.LG cs.AI cs.NA | null | 1212.2471 | null | null | http://arxiv.org/pdf/1212.2471v1 | 2012-10-19T15:06:41Z | 2012-10-19T15:06:41Z | Monte Carlo Matrix Inversion Policy Evaluation | In 1950, Forsythe and Leibler (1950) introduced a statistical technique for
finding the inverse of a matrix by characterizing the elements of the matrix
inverse as expected values of a sequence of random walks. Barto and Duff (1994)
subsequently showed relations between this technique and standard dynamic
programming and temporal differencing methods. The advantage of the Monte Carlo
matrix inversion (MCMI) approach is that it scales better with respect to
state-space size than alternative techniques. In this paper, we introduce an
algorithm for performing reinforcement learning policy evaluation using MCMI.
We demonstrate that MCMI improves on runtime over a maximum likelihood
model-based policy evaluation approach and on both runtime and accuracy over
the temporal differencing (TD) policy evaluation approach. We further improve
on MCMI policy evaluation by adding an importance sampling technique to our
algorithm to reduce the variance of our estimator. Lastly, we illustrate
techniques for scaling up MCMI to large state spaces in order to perform policy
improvement.
| [
"Fletcher Lu, Dale Schuurmans",
"['Fletcher Lu' 'Dale Schuurmans']"
] |
cs.LG stat.ML | null | 1212.2472 | null | null | http://arxiv.org/pdf/1212.2472v1 | 2012-10-19T15:06:36Z | 2012-10-19T15:06:36Z | Budgeted Learning of Naive-Bayes Classifiers | Frequently, acquiring training data has an associated cost. We consider the
situation where the learner may purchase data during training, subject TO a
budget. IN particular, we examine the CASE WHERE each feature label has an
associated cost, AND the total cost OF ALL feature labels acquired during
training must NOT exceed the budget.This paper compares methods FOR choosing
which feature label TO purchase next, given the budget AND the CURRENT belief
state OF naive Bayes model parameters.Whereas active learning has traditionally
focused ON myopic(greedy) strategies FOR query selection, this paper presents a
tractable method FOR incorporating knowledge OF the budget INTO the decision
making process, which improves performance.
| [
"Daniel J. Lizotte, Omid Madani, Russell Greiner",
"['Daniel J. Lizotte' 'Omid Madani' 'Russell Greiner']"
] |
cs.LG stat.ML | null | 1212.2474 | null | null | http://arxiv.org/pdf/1212.2474v1 | 2012-10-19T15:06:27Z | 2012-10-19T15:06:27Z | Learning Riemannian Metrics | We propose a solution to the problem of estimating a Riemannian metric
associated with a given differentiable manifold. The metric learning problem is
based on minimizing the relative volume of a given set of points. We derive the
details for a family of metrics on the multinomial simplex. The resulting
metric has applications in text classification and bears some similarity to
TFIDF representation of text documents.
| [
"Guy Lebanon",
"['Guy Lebanon']"
] |
cs.LG cs.SY | null | 1212.2475 | null | null | http://arxiv.org/pdf/1212.2475v1 | 2012-10-19T15:06:23Z | 2012-10-19T15:06:23Z | Efficient Gradient Estimation for Motor Control Learning | The task of estimating the gradient of a function in the presence of noise is
central to several forms of reinforcement learning, including policy search
methods. We present two techniques for reducing gradient estimation errors in
the presence of observable input noise applied to the control signal. The first
method extends the idea of a reinforcement baseline by fitting a local linear
model to the function whose gradient is being estimated; we show how to find
the linear model that minimizes the variance of the gradient estimate, and how
to estimate the model from data. The second method improves this further by
discounting components of the gradient vector that have high variance. These
methods are applied to the problem of motor control learning, where actuator
noise has a significant influence on behavior. In particular, we apply the
techniques to learn locally optimal controllers for a dart-throwing task using
a simulated three-link arm; we demonstrate that proposed methods significantly
improve the reward function gradient estimate and, consequently, the learning
curve, over existing methods.
| [
"Gregory Lawrence, Noah Cowan, Stuart Russell",
"['Gregory Lawrence' 'Noah Cowan' 'Stuart Russell']"
] |
cs.LG cs.AI stat.ML | null | 1212.2480 | null | null | http://arxiv.org/pdf/1212.2480v1 | 2012-10-19T15:06:00Z | 2012-10-19T15:06:00Z | Approximate Inference and Constrained Optimization | Loopy and generalized belief propagation are popular algorithms for
approximate inference in Markov random fields and Bayesian networks. Fixed
points of these algorithms correspond to extrema of the Bethe and Kikuchi free
energy. However, belief propagation does not always converge, which explains
the need for approaches that explicitly minimize the Kikuchi/Bethe free energy,
such as CCCP and UPS. Here we describe a class of algorithms that solves this
typically nonconvex constrained minimization of the Kikuchi free energy through
a sequence of convex constrained minimizations of upper bounds on the Kikuchi
free energy. Intuitively one would expect tighter bounds to lead to faster
algorithms, which is indeed convincingly demonstrated in our simulations.
Several ideas are applied to obtain tight convex bounds that yield dramatic
speed-ups over CCCP.
| [
"Tom Heskes, Kees Albers, Hilbert Kappen",
"['Tom Heskes' 'Kees Albers' 'Hilbert Kappen']"
] |
cs.LG stat.ML | null | 1212.2483 | null | null | http://arxiv.org/pdf/1212.2483v1 | 2012-10-19T15:05:46Z | 2012-10-19T15:05:46Z | Sufficient Dimensionality Reduction with Irrelevant Statistics | The problem of finding a reduced dimensionality representation of categorical
variables while preserving their most relevant characteristics is fundamental
for the analysis of complex data. Specifically, given a co-occurrence matrix of
two variables, one often seeks a compact representation of one variable which
preserves information about the other variable. We have recently introduced
``Sufficient Dimensionality Reduction' [GT-2003], a method that extracts
continuous reduced dimensional features whose measurements (i.e., expectation
values) capture maximal mutual information among the variables. However, such
measurements often capture information that is irrelevant for a given task.
Widely known examples are illumination conditions, which are irrelevant as
features for face recognition, writing style which is irrelevant as a feature
for content classification, and intonation which is irrelevant as a feature for
speech recognition. Such irrelevance cannot be deduced apriori, since it
depends on the details of the task, and is thus inherently ill defined in the
purely unsupervised case. Separating relevant from irrelevant features can be
achieved using additional side data that contains such irrelevant structures.
This approach was taken in [CT-2002], extending the information bottleneck
method, which uses clustering to compress the data. Here we use this
side-information framework to identify features whose measurements are
maximally informative for the original data set, but carry as little
information as possible on a side data set. In statistical terms this can be
understood as extracting statistics which are maximally sufficient for the
original dataset, while simultaneously maximally ancillary for the side
dataset. We formulate this tradeoff as a constrained optimization problem and
characterize its solutions. We then derive a gradient descent algorithm for
this problem, which is based on the Generalized Iterative Scaling method for
finding maximum entropy distributions. The method is demonstrated on synthetic
data, as well as on real face recognition datasets, and is shown to outperform
standard methods such as oriented PCA.
| [
"Amir Globerson, Gal Chechik, Naftali Tishby",
"['Amir Globerson' 'Gal Chechik' 'Naftali Tishby']"
] |
cs.LG stat.ML | null | 1212.2487 | null | null | http://arxiv.org/pdf/1212.2487v1 | 2012-10-19T15:05:29Z | 2012-10-19T15:05:29Z | Locally Weighted Naive Bayes | Despite its simplicity, the naive Bayes classifier has surprised machine
learning researchers by exhibiting good performance on a variety of learning
problems. Encouraged by these results, researchers have looked to overcome
naive Bayes primary weakness - attribute independence - and improve the
performance of the algorithm. This paper presents a locally weighted version of
naive Bayes that relaxes the independence assumption by learning local models
at prediction time. Experimental results show that locally weighted naive Bayes
rarely degrades accuracy compared to standard naive Bayes and, in many cases,
improves accuracy dramatically. The main advantage of this method compared to
other techniques for enhancing naive Bayes is its conceptual and computational
simplicity.
| [
"['Eibe Frank' 'Mark Hall' 'Bernhard Pfahringer']",
"Eibe Frank, Mark Hall, Bernhard Pfahringer"
] |
cs.LG stat.ML | null | 1212.2488 | null | null | http://arxiv.org/pdf/1212.2488v1 | 2012-10-19T15:05:25Z | 2012-10-19T15:05:25Z | A Distance-Based Branch and Bound Feature Selection Algorithm | There is no known efficient method for selecting k Gaussian features from n
which achieve the lowest Bayesian classification error. We show an example of
how greedy algorithms faced with this task are led to give results that are not
optimal. This motivates us to propose a more robust approach. We present a
Branch and Bound algorithm for finding a subset of k independent Gaussian
features which minimizes the naive Bayesian classification error. Our algorithm
uses additive monotonic distance measures to produce bounds for the Bayesian
classification error in order to exclude many feature subsets from evaluation,
while still returning an optimal solution. We test our method on synthetic data
as well as data obtained from gene expression profiling.
| [
"['Ari Frank' 'Dan Geiger' 'Zohar Yakhini']",
"Ari Frank, Dan Geiger, Zohar Yakhini"
] |
cs.LG stat.ML | null | 1212.2490 | null | null | http://arxiv.org/pdf/1212.2490v1 | 2012-10-19T15:07:56Z | 2012-10-19T15:07:56Z | On the Convergence of Bound Optimization Algorithms | Many practitioners who use the EM algorithm complain that it is sometimes
slow. When does this happen, and what can be done about it? In this paper, we
study the general class of bound optimization algorithms - including
Expectation-Maximization, Iterative Scaling and CCCP - and their relationship
to direct optimization algorithms such as gradient-based methods for parameter
learning. We derive a general relationship between the updates performed by
bound optimization methods and those of gradient and second-order methods and
identify analytic conditions under which bound optimization algorithms exhibit
quasi-Newton behavior, and conditions under which they possess poor,
first-order convergence. Based on this analysis, we consider several specific
algorithms, interpret and analyze their convergence properties and provide some
recipes for preprocessing input to these algorithms to yield faster convergence
behavior. We report empirical results supporting our analysis and showing that
simple data preprocessing can result in dramatically improved performance of
bound optimizers in practice.
| [
"Ruslan R Salakhutdinov, Sam T Roweis, Zoubin Ghahramani",
"['Ruslan R Salakhutdinov' 'Sam T Roweis' 'Zoubin Ghahramani']"
] |
cs.LG stat.ML | null | 1212.2491 | null | null | http://arxiv.org/pdf/1212.2491v1 | 2012-10-19T15:07:51Z | 2012-10-19T15:07:51Z | Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for
Latent Models | We present and implement two algorithms for analytic asymptotic evaluation of
the marginal likelihood of data given a Bayesian network with hidden nodes. As
shown by previous work, this evaluation is particularly hard for latent
Bayesian network models, namely networks that include hidden variables, where
asymptotic approximation deviates from the standard BIC score. Our algorithms
solve two central difficulties in asymptotic evaluation of marginal likelihood
integrals, namely, evaluation of regular dimensionality drop for latent
Bayesian network models and computation of non-standard approximation formulas
for singular statistics for these models. The presented algorithms are
implemented in Matlab and Maple and their usage is demonstrated for marginal
likelihood approximations for Bayesian networks with hidden variables.
| [
"['Dmitry Rusakov' 'Dan Geiger']",
"Dmitry Rusakov, Dan Geiger"
] |
cs.LG stat.ML | null | 1212.2494 | null | null | http://arxiv.org/pdf/1212.2494v1 | 2012-10-19T15:07:42Z | 2012-10-19T15:07:42Z | Learning Generative Models of Similarity Matrices | We describe a probabilistic (generative) view of affinity matrices along with
inference algorithms for a subclass of problems associated with data
clustering. This probabilistic view is helpful in understanding different
models and algorithms that are based on affinity functions OF the data. IN
particular, we show how(greedy) inference FOR a specific probabilistic model IS
equivalent TO the spectral clustering algorithm.It also provides a framework
FOR developing new algorithms AND extended models. AS one CASE, we present new
generative data clustering models that allow us TO infer the underlying
distance measure suitable for the clustering problem at hand. These models seem
to perform well in a larger class of problems for which other clustering
algorithms (including spectral clustering) usually fail. Experimental
evaluation was performed in a variety point data sets, showing excellent
performance.
| [
"Romer Rosales, Brendan J. Frey",
"['Romer Rosales' 'Brendan J. Frey']"
] |
cs.LG stat.ML | null | 1212.2498 | null | null | http://arxiv.org/pdf/1212.2498v1 | 2012-10-19T15:07:23Z | 2012-10-19T15:07:23Z | Learning Continuous Time Bayesian Networks | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is a function of its parents. We address the problem of learning
parameters and structure of a CTBN from fully observed data. We define a
conjugate prior for CTBNs, and show how it can be used both for Bayesian
parameter estimation and as the basis of a Bayesian score for structure
learning. Because acyclicity is not a constraint in CTBNs, we can show that the
structure learning problem is significantly easier, both in theory and in
practice, than structure learning for dynamic Bayesian networks (DBNs).
Furthermore, as CTBNs can tailor the parameters and dependency structure to the
different time granularities of the evolution of different variables, they can
provide a better fit to continuous-time processes than DBNs with a fixed time
granularity.
| [
"['Uri Nodelman' 'Christian R. Shelton' 'Daphne Koller']",
"Uri Nodelman, Christian R. Shelton, Daphne Koller"
] |
cs.LG cs.AI stat.ML | null | 1212.2500 | null | null | http://arxiv.org/pdf/1212.2500v1 | 2012-10-19T15:07:12Z | 2012-10-19T15:07:12Z | On Local Optima in Learning Bayesian Networks | This paper proposes and evaluates the k-greedy equivalence search algorithm
(KES) for learning Bayesian networks (BNs) from complete data. The main
characteristic of KES is that it allows a trade-off between greediness and
randomness, thus exploring different good local optima. When greediness is set
at maximum, KES corresponds to the greedy equivalence search algorithm (GES).
When greediness is kept at minimum, we prove that under mild assumptions KES
asymptotically returns any inclusion optimal BN with nonzero probability.
Experimental results for both synthetic and real data are reported showing that
KES often finds a better local optima than GES. Moreover, we use KES to
experimentally confirm that the number of different local optima is often huge.
| [
"Jens D. Nielsen, Tomas Kocka, Jose M. Pena",
"['Jens D. Nielsen' 'Tomas Kocka' 'Jose M. Pena']"
] |
cs.LG stat.ML | null | 1212.2504 | null | null | http://arxiv.org/pdf/1212.2504v1 | 2012-10-19T15:06:52Z | 2012-10-19T15:06:52Z | Efficiently Inducing Features of Conditional Random Fields | Conditional Random Fields (CRFs) are undirected graphical models, a special
case of which correspond to conditionally-trained finite state machines. A key
advantage of these models is their great flexibility to include a wide array of
overlapping, multi-granularity, non-independent features of the input. In face
of this freedom, an important question that remains is, what features should be
used? This paper presents a feature induction method for CRFs. Founded on the
principle of constructing only those feature conjunctions that significantly
increase log-likelihood, the approach is based on that of Della Pietra et al
[1997], but altered to work with conditional rather than joint probabilities,
and with additional modifications for providing tractability specifically for a
sequence model. In comparison with traditional approaches, automated feature
induction offers both improved accuracy and more than an order of magnitude
reduction in feature count; it enables the use of richer, higher-order Markov
models, and offers more freedom to liberally guess about which atomic features
may be relevant to a task. The induction method applies to linear-chain CRFs,
as well as to more arbitrary CRF structures, also known as Relational Markov
Networks [Taskar & Koller, 2002]. We present experimental results on a named
entity extraction task.
| [
"Andrew McCallum",
"['Andrew McCallum']"
] |
cs.LG cs.IR stat.ML | null | 1212.2508 | null | null | http://arxiv.org/pdf/1212.2508v1 | 2012-10-19T15:08:51Z | 2012-10-19T15:08:51Z | Collaborative Ensemble Learning: Combining Collaborative and
Content-Based Information Filtering via Hierarchical Bayes | Collaborative filtering (CF) and content-based filtering (CBF) have widely
been used in information filtering applications. Both approaches have their
strengths and weaknesses which is why researchers have developed hybrid
systems. This paper proposes a novel approach to unify CF and CBF in a
probabilistic framework, named collaborative ensemble learning. It uses
probabilistic SVMs to model each user's profile (as CBF does).At the prediction
phase, it combines a society OF users profiles, represented by their respective
SVM models, to predict an active users preferences(the CF idea).The combination
scheme is embedded in a probabilistic framework and retains an intuitive
explanation.Moreover, collaborative ensemble learning does not require a global
training stage and thus can incrementally incorporate new data.We report
results based on two data sets. For the Reuters-21578 text data set, we
simulate user ratings under the assumption that each user is interested in only
one category. In the second experiment, we use users' opinions on a set of 642
art images that were collected through a web-based survey. For both data sets,
collaborative ensemble achieved excellent performance in terms of
recommendation accuracy.
| [
"Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, HongJiang Zhang",
"['Kai Yu' 'Anton Schwaighofer' 'Volker Tresp' 'Wei-Ying Ma'\n 'HongJiang Zhang']"
] |
cs.LG stat.ML | null | 1212.2510 | null | null | http://arxiv.org/pdf/1212.2510v1 | 2012-10-19T15:08:42Z | 2012-10-19T15:08:42Z | Markov Random Walk Representations with Continuous Distributions | Representations based on random walks can exploit discrete data distributions
for clustering and classification. We extend such representations from discrete
to continuous distributions. Transition probabilities are now calculated using
a diffusion equation with a diffusion coefficient that inversely depends on the
data density. We relate this diffusion equation to a path integral and derive
the corresponding path probability measure. The framework is useful for
incorporating continuous data densities and prior knowledge.
| [
"['Chen-Hsiang Yeang' 'Martin Szummer']",
"Chen-Hsiang Yeang, Martin Szummer"
] |
cs.LG stat.ML | null | 1212.2511 | null | null | http://arxiv.org/pdf/1212.2511v1 | 2012-10-19T15:08:38Z | 2012-10-19T15:08:38Z | Stochastic complexity of Bayesian networks | Bayesian networks are now being used in enormous fields, for example,
diagnosis of a system, data mining, clustering and so on. In spite of their
wide range of applications, the statistical properties have not yet been
clarified, because the models are nonidentifiable and non-regular. In a
Bayesian network, the set of its parameter for a smaller model is an analytic
set with singularities in the space of large ones. Because of these
singularities, the Fisher information matrices are not positive definite. In
other words, the mathematical foundation for learning was not constructed. In
recent years, however, we have developed a method to analyze non-regular models
using algebraic geometry. This method revealed the relation between the models
singularities and its statistical properties. In this paper, applying this
method to Bayesian networks with latent variables, we clarify the order of the
stochastic complexities.Our result claims that the upper bound of those is
smaller than the dimension of the parameter space. This means that the Bayesian
generalization error is also far smaller than that of regular model, and that
Schwarzs model selection criterion BIC needs to be improved for Bayesian
networks.
| [
"Keisuke Yamazaki, Sumio Watanbe",
"['Keisuke Yamazaki' 'Sumio Watanbe']"
] |
cs.LG stat.ML | null | 1212.2512 | null | null | http://arxiv.org/pdf/1212.2512v1 | 2012-10-19T15:08:33Z | 2012-10-19T15:08:33Z | A Generalized Mean Field Algorithm for Variational Inference in
Exponential Families | The mean field methods, which entail approximating intractable probability
distributions variationally with distributions from a tractable family, enjoy
high efficiency, guaranteed convergence, and provide lower bounds on the true
likelihood. But due to requirement for model-specific derivation of the
optimization equations and unclear inference quality in various models, it is
not widely used as a generic approximate inference algorithm. In this paper, we
discuss a generalized mean field theory on variational approximation to a broad
class of intractable distributions using a rich set of tractable distributions
via constrained optimization over distribution spaces. We present a class of
generalized mean field (GMF) algorithms for approximate inference in complex
exponential family models, which entails limiting the optimization over the
class of cluster-factorizable distributions. GMF is a generic method requiring
no model-specific derivations. It factors a complex model into a set of
disjoint variable clusters, and uses a set of canonical fix-point equations to
iteratively update the cluster distributions, and converge to locally optimal
cluster marginals that preserve the original dependency structure within each
cluster, hence, fully decomposed the overall inference problem. We empirically
analyzed the effect of different tractable family (clusters of different
granularity) on inference quality, and compared GMF with BP on several
canonical models. Possible extension to higher-order MF approximation is also
discussed.
| [
"['Eric P. Xing' 'Michael I. Jordan' 'Stuart Russell']",
"Eric P. Xing, Michael I. Jordan, Stuart Russell"
] |
cs.LG stat.ML | null | 1212.2513 | null | null | http://arxiv.org/pdf/1212.2513v1 | 2012-10-19T15:08:28Z | 2012-10-19T15:08:28Z | Efficient Parametric Projection Pursuit Density Estimation | Product models of low dimensional experts are a powerful way to avoid the
curse of dimensionality. We present the ``under-complete product of experts'
(UPoE), where each expert models a one dimensional projection of the data. The
UPoE is fully tractable and may be interpreted as a parametric probabilistic
model for projection pursuit. Its ML learning rules are identical to the
approximate learning rules proposed before for under-complete ICA. We also
derive an efficient sequential learning algorithm and discuss its relationship
to projection pursuit density estimation and feature induction algorithms for
additive random field models.
| [
"Max Welling, Richard S. Zemel, Geoffrey E. Hinton",
"['Max Welling' 'Richard S. Zemel' 'Geoffrey E. Hinton']"
] |
cs.LG stat.ML | null | 1212.2514 | null | null | http://arxiv.org/pdf/1212.2514v1 | 2012-10-19T15:08:24Z | 2012-10-19T15:08:24Z | Boltzmann Machine Learning with the Latent Maximum Entropy Principle | We present a new statistical learning paradigm for Boltzmann machines based
on a new inference principle we have proposed: the latent maximum entropy
principle (LME). LME is different both from Jaynes maximum entropy principle
and from standard maximum likelihood estimation.We demonstrate the LME
principle BY deriving new algorithms for Boltzmann machine parameter
estimation, and show how robust and fast new variant of the EM algorithm can be
developed.Our experiments show that estimation based on LME generally yields
better results than maximum likelihood estimation, particularly when inferring
hidden units from small amounts of data.
| [
"['Shaojun Wang' 'Dale Schuurmans' 'Fuchun Peng' 'Yunxin Zhao']",
"Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao"
] |
cs.LG stat.ML | null | 1212.2516 | null | null | http://arxiv.org/pdf/1212.2516v1 | 2012-10-19T15:08:15Z | 2012-10-19T15:08:15Z | Learning Measurement Models for Unobserved Variables | Observed associations in a database may be due in whole or part to variations
in unrecorded (latent) variables. Identifying such variables and their causal
relationships with one another is a principal goal in many scientific and
practical domains. Previous work shows that, given a partition of observed
variables such that members of a class share only a single latent common cause,
standard search algorithms for causal Bayes nets can infer structural relations
between latent variables. We introduce an algorithm for discovering such
partitions when they exist. Uniquely among available procedures, the algorithm
is (asymptotically) correct under standard assumptions in causal Bayes net
search algorithms, requires no prior knowledge of the number of latent
variables, and does not depend on the mathematical form of the relationships
among the latent variables. We evaluate the algorithm on a variety of simulated
data sets.
| [
"['Ricardo Silva' 'Richard Scheines' 'Clark Glymour' 'Peter L. Spirtes']",
"Ricardo Silva, Richard Scheines, Clark Glymour, Peter L. Spirtes"
] |
cs.LG cs.CE stat.ML | null | 1212.2517 | null | null | http://arxiv.org/pdf/1212.2517v1 | 2012-10-19T15:08:06Z | 2012-10-19T15:08:06Z | Learning Module Networks | Methods for learning Bayesian network structure can discover dependency
structure between observed variables, and have been shown to be useful in many
applications. However, in domains that involve a large number of variables, the
space of possible network structures is enormous, making it difficult, for both
computational and statistical reasons, to identify a good model. In this paper,
we consider a solution to this problem, suitable for domains where many
variables have similar behavior. Our method is based on a new class of models,
which we call module networks. A module network explicitly represents the
notion of a module - a set of variables that have the same parents in the
network and share the same conditional probability distribution. We define the
semantics of module networks, and describe an algorithm that learns a module
network from data. The algorithm learns both the partitioning of the variables
into modules and the dependency structure between the variables. We evaluate
our algorithm on synthetic data, and on real data in the domains of gene
expression and the stock market. Our results show that module networks
generalize better than Bayesian networks, and that the learned module network
structure reveals regularities that are obscured in learned Bayesian networks.
| [
"Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman",
"['Eran Segal' \"Dana Pe'er\" 'Aviv Regev' 'Daphne Koller' 'Nir Friedman']"
] |
cs.LG cs.DS stat.ML | null | 1212.2573 | null | null | http://arxiv.org/pdf/1212.2573v1 | 2012-12-11T18:22:31Z | 2012-12-11T18:22:31Z | Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs | We consider the problem of learning the structure of undirected graphical
models with bounded treewidth, within the maximum likelihood framework. This is
an NP-hard problem and most approaches consider local search techniques. In
this paper, we pose it as a combinatorial optimization problem, which is then
relaxed to a convex optimization problem that involves searching over the
forest and hyperforest polytopes with special structures, independently. A
supergradient method is used to solve the dual problem, with a run-time
complexity of $O(k^3 n^{k+2} \log n)$ for each iteration, where $n$ is the
number of variables and $k$ is a bound on the treewidth. We compare our
approach to state-of-the-art methods on synthetic datasets and classical
benchmarks, showing the gains of the novel convex approach.
| [
"['K. S. Sesh Kumar' 'Francis Bach']",
"K. S. Sesh Kumar (LIENS, INRIA Paris - Rocquencourt), Francis Bach\n (LIENS, INRIA Paris - Rocquencourt)"
] |
q-bio.QM cs.LG stat.AP | null | 1212.2617 | null | null | http://arxiv.org/pdf/1212.2617v1 | 2012-12-11T20:33:16Z | 2012-12-11T20:33:16Z | Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on
support vector machine classification of RT-QuIC data | In this work we study numerical construction of optimal clinical diagnostic
tests for detecting sporadic Creutzfeldt-Jakob disease (sCJD). A cerebrospinal
fluid sample (CSF) from a suspected sCJD patient is subjected to a process
which initiates the aggregation of a protein present only in cases of sCJD.
This aggregation is indirectly observed in real-time at regular intervals, so
that a longitudinal set of data is constructed that is then analysed for
evidence of this aggregation. The best existing test is based solely on the
final value of this set of data, which is compared against a threshold to
conclude whether or not aggregation, and thus sCJD, is present. This test
criterion was decided upon by analysing data from a total of 108 sCJD and
non-sCJD samples, but this was done subjectively and there is no supporting
mathematical analysis declaring this criterion to be exploiting the available
data optimally. This paper addresses this deficiency, seeking to validate or
improve the test primarily via support vector machine (SVM) classification.
Besides this, we address a number of additional issues such as i) early
stopping of the measurement process, ii) the possibility of detecting the
particular type of sCJD and iii) the incorporation of additional patient data
such as age, sex, disease duration and timing of CSF sampling into the
construction of the test.
| [
"William Hulme, Peter Richt\\'arik, Lynne McGuire and Alison Green",
"['William Hulme' 'Peter Richtárik' 'Lynne McGuire' 'Alison Green']"
] |
stat.ML cs.LG | null | 1212.2686 | null | null | http://arxiv.org/pdf/1212.2686v1 | 2012-12-12T01:59:27Z | 2012-12-12T01:59:27Z | Joint Training of Deep Boltzmann Machines | We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks.
| [
"Ian Goodfellow, Aaron Courville, Yoshua Bengio",
"['Ian Goodfellow' 'Aaron Courville' 'Yoshua Bengio']"
] |
stat.ML cond-mat.stat-mech cs.LG | null | 1212.2767 | null | null | http://arxiv.org/pdf/1212.2767v1 | 2012-12-12T10:55:27Z | 2012-12-12T10:55:27Z | Bayesian one-mode projection for dynamic bipartite graphs | We propose a Bayesian methodology for one-mode projecting a bipartite network
that is being observed across a series of discrete time steps. The resulting
one mode network captures the uncertainty over the presence/absence of each
link and provides a probability distribution over its possible weight values.
Additionally, the incorporation of prior knowledge over previous states makes
the resulting network less sensitive to noise and missing observations that
usually take place during the data collection process. The methodology consists
of computationally inexpensive update rules and is scalable to large problems,
via an appropriate distributed implementation.
| [
"Ioannis Psorakis, Iead Rezek, Zach Frankel, Stephen J. Roberts",
"['Ioannis Psorakis' 'Iead Rezek' 'Zach Frankel' 'Stephen J. Roberts']"
] |
cs.LG math.OC stat.ML | null | 1212.2834 | null | null | http://arxiv.org/pdf/1212.2834v2 | 2013-06-10T09:31:45Z | 2012-12-12T15:02:20Z | Dictionary Subselection Using an Overcomplete Joint Sparsity Model | Many natural signals exhibit a sparse representation, whenever a suitable
describing model is given. Here, a linear generative model is considered, where
many sparsity-based signal processing techniques rely on such a simplified
model. As this model is often unknown for many classes of the signals, we need
to select such a model based on the domain knowledge or using some exemplar
signals. This paper presents a new exemplar based approach for the linear model
(called the dictionary) selection, for such sparse inverse problems. The
problem of dictionary selection, which has also been called the dictionary
learning in this setting, is first reformulated as a joint sparsity model. The
joint sparsity model here differs from the standard joint sparsity model as it
considers an overcompleteness in the representation of each signal, within the
range of selected subspaces. The new dictionary selection paradigm is examined
with some synthetic and realistic simulations.
| [
"['Mehrdad Yaghoobi' 'Laurent Daudet' 'Michael E. Davies']",
"Mehrdad Yaghoobi, Laurent Daudet, Michael E. Davies"
] |
cs.LG | null | 1212.3185 | null | null | http://arxiv.org/pdf/1212.3185v3 | 2013-06-03T02:42:35Z | 2012-12-13T14:31:58Z | Cost-Sensitive Feature Selection of Data with Errors | In data mining applications, feature selection is an essential process since
it reduces a model's complexity. The cost of obtaining the feature values must
be taken into consideration in many domains. In this paper, we study the
cost-sensitive feature selection problem on numerical data with measurement
errors, test costs and misclassification costs. The major contributions of this
paper are four-fold. First, a new data model is built to address test costs and
misclassification costs as well as error boundaries. Second, a covering-based
rough set with measurement errors is constructed. Given a confidence interval,
the neighborhood is an ellipse in a two-dimension space, or an ellipsoidal in a
three-dimension space, etc. Third, a new cost-sensitive feature selection
problem is defined on this covering-based rough set. Fourth, both backtracking
and heuristic algorithms are proposed to deal with this new problem. The
algorithms are tested on six UCI (University of California - Irvine) data sets.
Experimental results show that (1) the pruning techniques of the backtracking
algorithm help reducing the number of operations significantly, and (2) the
heuristic algorithm usually obtains optimal results. This study is a step
toward realistic applications of cost-sensitive learning.
| [
"['Hong Zhao' 'Fan Min' 'William Zhu']",
"Hong Zhao, Fan Min and William Zhu"
] |
stat.ML cs.LG | null | 1212.3276 | null | null | http://arxiv.org/pdf/1212.3276v3 | 2016-04-18T09:17:36Z | 2012-12-13T19:20:21Z | Learning Sparse Low-Threshold Linear Classifiers | We consider the problem of learning a non-negative linear classifier with a
$1$-norm of at most $k$, and a fixed threshold, under the hinge-loss. This
problem generalizes the problem of learning a $k$-monotone disjunction. We
prove that we can learn efficiently in this setting, at a rate which is linear
in both $k$ and the size of the threshold, and that this is the best possible
rate. We provide an efficient online learning algorithm that achieves the
optimal rate, and show that in the batch case, empirical risk minimization
achieves this rate as well. The rates we show are tighter than the uniform
convergence rate, which grows with $k^2$.
| [
"['Sivan Sabato' 'Shai Shalev-Shwartz' 'Nathan Srebro' 'Daniel Hsu'\n 'Tong Zhang']",
"Sivan Sabato and Shai Shalev-Shwartz and Nathan Srebro and Daniel Hsu\n and Tong Zhang"
] |
cs.LG cs.IR | null | 1212.3390 | null | null | http://arxiv.org/pdf/1212.3390v1 | 2012-12-14T04:12:21Z | 2012-12-14T04:12:21Z | Know Your Personalization: Learning Topic level Personalization in
Online Services | Online service platforms (OSPs), such as search engines, news-websites,
ad-providers, etc., serve highly pe rsonalized content to the user, based on
the profile extracted from his history with the OSP. Although personalization
(generally) leads to a better user experience, it also raises privacy concerns
for the user---he does not know what is present in his profile and more
importantly, what is being used to per sonalize content for him. In this paper,
we capture OSP's personalization for an user in a new data structure called the
person alization vector ($\eta$), which is a weighted vector over a set of
topics, and present techniques to compute it for users of an OSP. Our approach
treats OSPs as black-boxes, and extracts $\eta$ by mining only their output,
specifical ly, the personalized (for an user) and vanilla (without any user
information) contents served, and the differences in these content. We
formulate a new model called Latent Topic Personalization (LTP) that captures
the personalization vector into a learning framework and present efficient
inference algorithms for it. We do extensive experiments for search result
personalization using both data from real Google users and synthetic datasets.
Our results show high accuracy (R-pre = 84%) of LTP in finding personalized
topics. For Google data, our qualitative results show how LTP can also
identifies evidences---queries for results on a topic with high $\eta$ value
were re-ranked. Finally, we show how our approach can be used to build a new
Privacy evaluation framework focused at end-user privacy on commercial OSPs.
| [
"['Anirban Majumder' 'Nisheeth Shrivastava']",
"Anirban Majumder and Nisheeth Shrivastava"
] |
cs.LO cs.FL cs.LG cs.SE | 10.4204/EPTCS.103 | 1212.3454 | null | null | http://arxiv.org/abs/1212.3454v1 | 2012-12-14T12:38:37Z | 2012-12-14T12:38:37Z | Proceedings Quantities in Formal Methods | This volume contains the proceedings of the Workshop on Quantities in Formal
Methods, QFM 2012, held in Paris, France on 28 August 2012. The workshop was
affiliated with the 18th Symposium on Formal Methods, FM 2012. The focus of the
workshop was on quantities in modeling, verification, and synthesis. Modern
applications of formal methods require to reason formally on quantities such as
time, resources, or probabilities. Standard formal methods and tools have
gotten very good at modeling (and verifying) qualitative properties: whether or
not certain events will occur. During the last years, these methods and tools
have been extended to also cover quantitative aspects, notably leading to tools
like e.g. UPPAAL (for real-time systems), PRISM (for probabilistic systems),
and PHAVer (for hybrid systems). A lot of work remains to be done however
before these tools can be used in the industrial applications at which they are
aiming.
| [
"Uli Fahrenberg (Irisa / INRIA Rennes, France), Axel Legay (Irisa /\n INRIA Rennes, France), Claus Thrane (Aalborg University, Denmark)",
"['Uli Fahrenberg' 'Axel Legay' 'Claus Thrane']"
] |
cs.AI cs.LG cs.LO | 10.4204/EPTCS.118.2 | 1212.3618 | null | null | http://arxiv.org/abs/1212.3618v2 | 2013-07-08T05:19:38Z | 2012-12-14T21:06:34Z | Machine Learning in Proof General: Interfacing Interfaces | We present ML4PG - a machine learning extension for Proof General. It allows
users to gather proof statistics related to shapes of goals, sequences of
applied tactics, and proof tree structures from the libraries of interactive
higher-order proofs written in Coq and SSReflect. The gathered data is
clustered using the state-of-the-art machine learning algorithms available in
MATLAB and Weka. ML4PG provides automated interfacing between Proof General and
MATLAB/Weka. The results of clustering are used by ML4PG to provide proof hints
in the process of interactive proof development.
| [
"Ekaterina Komendantskaya (School of Computing, University of Dundee),\n J\\'onathan Heras (School of Computing, University of Dundee), Gudmund Grov\n (School of Mathematical and Computer Sciences, Heriot-Watt University)",
"['Ekaterina Komendantskaya' 'Jónathan Heras' 'Gudmund Grov']"
] |
cs.LG | null | 1212.3631 | null | null | http://arxiv.org/pdf/1212.3631v1 | 2012-12-14T22:50:44Z | 2012-12-14T22:50:44Z | Learning efficient sparse and low rank models | Parsimony, including sparsity and low rank, has been shown to successfully
model data in numerous machine learning and signal processing tasks.
Traditionally, such modeling approaches rely on an iterative algorithm that
minimizes an objective function with parsimony-promoting terms. The inherently
sequential structure and data-dependent complexity and latency of iterative
optimization constitute a major limitation in many applications requiring
real-time performance or involving large-scale data. Another limitation
encountered by these modeling techniques is the difficulty of their inclusion
in discriminative learning scenarios. In this work, we propose to move the
emphasis from the model to the pursuit algorithm, and develop a process-centric
view of parsimonious modeling, in which a learned deterministic
fixed-complexity pursuit process is used in lieu of iterative optimization. We
show a principled way to construct learnable pursuit process architectures for
structured sparse and robust low rank models, derived from the iteration of
proximal descent algorithms. These architectures learn to approximate the exact
parsimonious representation at a fraction of the complexity of the standard
optimization methods. We also show that appropriate training regimes allow to
naturally extend parsimonious models to discriminative settings.
State-of-the-art results are demonstrated on several challenging problems in
image and audio processing with several orders of magnitude speedup compared to
the exact optimization algorithms.
| [
"['Pablo Sprechmann' 'Alex M. Bronstein' 'Guillermo Sapiro']",
"Pablo Sprechmann, Alex M. Bronstein and Guillermo Sapiro"
] |
cs.SE cs.LG | null | 1212.3669 | null | null | http://arxiv.org/pdf/1212.3669v2 | 2014-07-21T21:11:36Z | 2012-12-15T09:53:16Z | A metric for software vulnerabilities classification | Vulnerability discovery and exploits detection are two wide areas of study in
software engineering. This preliminary work tries to combine existing methods
with machine learning techniques to define a metric classification of
vulnerable computer programs. First a feature set has been defined and later
two models have been tested against real world vulnerabilities. A relation
between the classifier choice and the features has also been outlined.
| [
"['Gabriele Modena']",
"Gabriele Modena"
] |
cs.LG cs.NE q-bio.NC | 10.1109/TCSI.2012.2206463 | 1212.3765 | null | null | http://arxiv.org/abs/1212.3765v1 | 2012-12-16T09:05:02Z | 2012-12-16T09:05:02Z | Biologically Inspired Spiking Neurons : Piecewise Linear Models and
Digital Implementation | There has been a strong push recently to examine biological scale simulations
of neuromorphic algorithms to achieve stronger inference capabilities. This
paper presents a set of piecewise linear spiking neuron models, which can
reproduce different behaviors, similar to the biological neuron, both for a
single neuron as well as a network of neurons. The proposed models are
investigated, in terms of digital implementation feasibility and costs,
targeting large scale hardware implementation. Hardware synthesis and physical
implementations on FPGA show that the proposed models can produce precise
neural behaviors with higher performance and considerably lower implementation
costs compared with the original model. Accordingly, a compact structure of the
models which can be trained with supervised and unsupervised learning
algorithms has been developed. Using this structure and based on a spike rate
coding, a character recognition case study has been implemented and tested.
| [
"['Hamid Soleimani' 'Arash Ahmadi' 'Mohammad Bavandpour']",
"Hamid Soleimani, Arash Ahmadi and Mohammad Bavandpour"
] |
cs.IT cs.LG math.IT stat.ML | null | 1212.3850 | null | null | http://arxiv.org/pdf/1212.3850v1 | 2012-12-16T23:22:56Z | 2012-12-16T23:22:56Z | Belief Propagation for Continuous State Spaces: Stochastic
Message-Passing with Quantitative Guarantees | The sum-product or belief propagation (BP) algorithm is a widely used
message-passing technique for computing approximate marginals in graphical
models. We introduce a new technique, called stochastic orthogonal series
message-passing (SOSMP), for computing the BP fixed point in models with
continuous random variables. It is based on a deterministic approximation of
the messages via orthogonal series expansion, and a stochastic approximation
via Monte Carlo estimates of the integral updates of the basis coefficients. We
prove that the SOSMP iterates converge to a \delta-neighborhood of the unique
BP fixed point for any tree-structured graph, and for any graphs with cycles in
which the BP updates satisfy a contractivity condition. In addition, we
demonstrate how to choose the number of basis coefficients as a function of the
desired approximation accuracy \delta and smoothness of the compatibility
functions. We illustrate our theory with both simulated examples and in
application to optical flow estimation.
| [
"['Nima Noorshams' 'Martin J. Wainwright']",
"Nima Noorshams and Martin J. Wainwright"
] |
cs.LG cs.LO cs.SE | 10.4204/EPTCS.103.6 | 1212.3873 | null | null | http://arxiv.org/abs/1212.3873v1 | 2012-12-17T03:40:47Z | 2012-12-17T03:40:47Z | Learning Markov Decision Processes for Model Checking | Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.
| [
"Hua Mao (AAU), Yingke Chen (AAU), Manfred Jaeger (AAU), Thomas D.\n Nielsen (AAU), Kim G. Larsen (AAU), Brian Nielsen (AAU)",
"['Hua Mao' 'Yingke Chen' 'Manfred Jaeger' 'Thomas D. Nielsen'\n 'Kim G. Larsen' 'Brian Nielsen']"
] |
stat.ML cs.LG | null | 1212.3900 | null | null | http://arxiv.org/pdf/1212.3900v2 | 2012-12-21T19:55:53Z | 2012-12-17T06:49:14Z | A Tutorial on Probabilistic Latent Semantic Analysis | In this tutorial, I will discuss the details about how Probabilistic Latent
Semantic Analysis (PLSA) is formalized and how different learning algorithms
are proposed to learn the model.
| [
"['Liangjie Hong']",
"Liangjie Hong"
] |
cs.CV cs.LG | 10.1109/TNNLS.2015.2487364 | 1212.3913 | null | null | http://arxiv.org/abs/1212.3913v4 | 2017-03-12T08:36:27Z | 2012-12-17T07:56:15Z | Group Component Analysis for Multiblock Data: Common and Individual
Feature Extraction | Very often data we encounter in practice is a collection of matrices rather
than a single matrix. These multi-block data are naturally linked and hence
often share some common features and at the same time they have their own
individual features, due to the background in which they are measured and
collected. In this study we proposed a new scheme of common and individual
feature analysis (CIFA) that processes multi-block data in a linked way aiming
at discovering and separating their common and individual features. According
to whether the number of common features is given or not, two efficient
algorithms were proposed to extract the common basis which is shared by all
data. Then feature extraction is performed on the common and the individual
spaces separately by incorporating the techniques such as dimensionality
reduction and blind source separation. We also discussed how the proposed CIFA
can significantly improve the performance of classification and clustering
tasks by exploiting common and individual features of samples respectively. Our
experimental results show some encouraging features of the proposed methods in
comparison to the state-of-the-art methods on synthetic and real data.
| [
"Guoxu Zhou and Andrzej Cichocki and Yu Zhang and Danilo Mandic",
"['Guoxu Zhou' 'Andrzej Cichocki' 'Yu Zhang' 'Danilo Mandic']"
] |
stat.ML cs.LG math.OC | null | 1212.4137 | null | null | http://arxiv.org/pdf/1212.4137v2 | 2020-05-07T00:50:36Z | 2012-12-17T20:53:35Z | Alternating Maximization: Unifying Framework for 8 Sparse PCA
Formulations and Efficient Parallel Codes | Given a multivariate data set, sparse principal component analysis (SPCA)
aims to extract several linear combinations of the variables that together
explain the variance in the data as much as possible, while controlling the
number of nonzero loadings in these combinations. In this paper we consider 8
different optimization formulations for computing a single sparse loading
vector; these are obtained by combining the following factors: we employ two
norms for measuring variance (L2, L1) and two sparsity-inducing norms (L0, L1),
which are used in two different ways (constraint, penalty). Three of our
formulations, notably the one with L0 constraint and L1 variance, have not been
considered in the literature. We give a unifying reformulation which we propose
to solve via a natural alternating maximization (AM) method. We show the the AM
method is nontrivially equivalent to GPower (Journ\'{e}e et al; JMLR
11:517--553, 2010) for all our formulations. Besides this, we provide 24
efficient parallel SPCA implementations: 3 codes (multi-core, GPU and cluster)
for each of the 8 problems. Parallelism in the methods is aimed at i) speeding
up computations (our GPU code can be 100 times faster than an efficient serial
code written in C++), ii) obtaining solutions explaining more variance and iii)
dealing with big data problems (our cluster code is able to solve a 357 GB
problem in about a minute).
| [
"['Peter Richtárik' 'Majid Jahani' 'Selin Damla Ahipaşaoğlu' 'Martin Takáč']",
"Peter Richt\\'arik, Majid Jahani, Selin Damla Ahipa\\c{s}ao\\u{g}lu,\n Martin Tak\\'a\\v{c}"
] |
stat.ML cs.DC cs.LG math.OC | null | 1212.4174 | null | null | http://arxiv.org/pdf/1212.4174v1 | 2012-12-17T21:43:31Z | 2012-12-17T21:43:31Z | Feature Clustering for Accelerating Parallel Coordinate Descent | Large-scale L1-regularized loss minimization problems arise in
high-dimensional applications such as compressed sensing and high-dimensional
supervised learning, including classification and regression problems.
High-performance algorithms and implementations are critical to efficiently
solving these problems. Building upon previous work on coordinate descent
algorithms for L1-regularized problems, we introduce a novel family of
algorithms called block-greedy coordinate descent that includes, as special
cases, several existing algorithms such as SCD, Greedy CD, Shotgun, and
Thread-Greedy. We give a unified convergence analysis for the family of
block-greedy algorithms. The analysis suggests that block-greedy coordinate
descent can better exploit parallelism if features are clustered so that the
maximum inner product between features in different blocks is small. Our
theoretical convergence analysis is supported with experimental re- sults using
data from diverse real-world applications. We hope that algorithmic approaches
and convergence analysis we provide will not only advance the field, but will
also encourage researchers to systematically explore the design space of
algorithms for solving large-scale L1-regularization problems.
| [
"Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin",
"['Chad Scherrer' 'Ambuj Tewari' 'Mahantesh Halappanavar' 'David Haglin']"
] |
cs.LG stat.ML | null | 1212.4347 | null | null | http://arxiv.org/pdf/1212.4347v1 | 2012-12-18T13:35:38Z | 2012-12-18T13:35:38Z | Bayesian Group Nonnegative Matrix Factorization for EEG Analysis | We propose a generative model of a group EEG analysis, based on appropriate
kernel assumptions on EEG data. We derive the variational inference update rule
using various approximation techniques. The proposed model outperforms the
current state-of-the-art algorithms in terms of common pattern extraction. The
validity of the proposed model is tested on the BCI competition dataset.
| [
"Bonggun Shin, Alice Oh",
"['Bonggun Shin' 'Alice Oh']"
] |
stat.ML cs.LG cs.NA | null | 1212.4507 | null | null | http://arxiv.org/pdf/1212.4507v2 | 2012-12-20T18:49:18Z | 2012-12-18T21:06:10Z | Variational Optimization | We discuss a general technique that can be used to form a differentiable
bound on the optima of non-differentiable or discrete objective functions. We
form a unified description of these methods and consider under which
circumstances the bound is concave. In particular we consider two concrete
applications of the method, namely sparse learning and support vector
classification.
| [
"['Joe Staines' 'David Barber']",
"Joe Staines and David Barber"
] |
cs.CV cs.IR cs.LG cs.MM | null | 1212.4522 | null | null | http://arxiv.org/pdf/1212.4522v2 | 2013-09-02T19:14:58Z | 2012-12-18T22:02:43Z | A Multi-View Embedding Space for Modeling Internet Images, Tags, and
their Semantics | This paper investigates the problem of modeling Internet images and
associated text or tags for tasks such as image-to-image search, tag-to-image
search, and image-to-tag search (image annotation). We start with canonical
correlation analysis (CCA), a popular and successful approach for mapping
visual and textual features to the same latent space, and incorporate a third
view capturing high-level image semantics, represented either by a single
category or multiple non-mutually-exclusive concepts. We present two ways to
train the three-view embedding: supervised, with the third view coming from
ground-truth labels or search keywords; and unsupervised, with semantic themes
automatically obtained by clustering the tags. To ensure high accuracy for
retrieval tasks while keeping the learning process scalable, we combine
multiple strong visual features and use explicit nonlinear kernel mappings to
efficiently approximate kernel CCA. To perform retrieval, we use a specially
designed similarity function in the embedded space, which substantially
outperforms the Euclidean distance. The resulting system produces compelling
qualitative results and outperforms a number of two-view baselines on retrieval
tasks on three large-scale Internet image datasets.
| [
"['Yunchao Gong' 'Qifa Ke' 'Michael Isard' 'Svetlana Lazebnik']",
"Yunchao Gong and Qifa Ke and Michael Isard and Svetlana Lazebnik"
] |
cs.LG | null | 1212.4675 | null | null | http://arxiv.org/pdf/1212.4675v1 | 2012-12-18T20:17:56Z | 2012-12-18T20:17:56Z | Analysis of Large-scale Traffic Dynamics using Non-negative Tensor
Factorization | In this paper, we present our work on clustering and prediction of temporal
dynamics of global congestion configurations in large-scale road networks.
Instead of looking into temporal traffic state variation of individual links,
or of small areas, we focus on spatial congestion configurations of the whole
network. In our work, we aim at describing the typical temporal dynamic
patterns of this network-level traffic state and achieving long-term prediction
of the large-scale traffic dynamics, in a unified data-mining framework. To
this end, we formulate this joint task using Non-negative Tensor Factorization
(NTF), which has been shown to be a useful decomposition tools for multivariate
data sequences. Clustering and prediction are performed based on the compact
tensor factorization results. Experiments on large-scale simulated data
illustrate the interest of our method with promising results for long-term
forecast of traffic evolution.
| [
"Yufei Han (INRIA Rocquencourt), Fabien Moutarde (CAOR)",
"['Yufei Han' 'Fabien Moutarde']"
] |
cs.CR cs.LG stat.ML | null | 1212.4775 | null | null | http://arxiv.org/pdf/1212.4775v3 | 2013-01-04T22:24:15Z | 2012-12-19T18:12:34Z | Role Mining with Probabilistic Models | Role mining tackles the problem of finding a role-based access control (RBAC)
configuration, given an access-control matrix assigning users to access
permissions as input. Most role mining approaches work by constructing a large
set of candidate roles and use a greedy selection strategy to iteratively pick
a small subset such that the differences between the resulting RBAC
configuration and the access control matrix are minimized. In this paper, we
advocate an alternative approach that recasts role mining as an inference
problem rather than a lossy compression problem. Instead of using combinatorial
algorithms to minimize the number of roles needed to represent the
access-control matrix, we derive probabilistic models to learn the RBAC
configuration that most likely underlies the given matrix.
Our models are generative in that they reflect the way that permissions are
assigned to users in a given RBAC configuration. We additionally model how
user-permission assignments that conflict with an RBAC configuration emerge and
we investigate the influence of constraints on role hierarchies and on the
number of assignments. In experiments with access-control matrices from
real-world enterprises, we compare our proposed models with other role mining
methods. Our results show that our probabilistic models infer roles that
generalize well to new system users for a wide variety of data, while other
models' generalization abilities depend on the dataset given.
| [
"Mario Frank, Joachim M. Buhmann, David Basin",
"['Mario Frank' 'Joachim M. Buhmann' 'David Basin']"
] |
cs.LG cs.DS stat.ML | null | 1212.4777 | null | null | http://arxiv.org/pdf/1212.4777v1 | 2012-12-19T18:14:51Z | 2012-12-19T18:14:51Z | A Practical Algorithm for Topic Modeling with Provable Guarantees | Topic models provide a useful method for dimensionality reduction and
exploratory data analysis in large text corpora. Most approaches to topic model
inference have been based on a maximum likelihood objective. Efficient
algorithms exist that approximate this objective, but they have no provable
guarantees. Recently, algorithms have been introduced that provide provable
bounds, but these algorithms are not practical because they are inefficient and
not robust to violations of model assumptions. In this paper we present an
algorithm for topic model inference that is both provable and practical. The
algorithm produces results comparable to the best MCMC implementations while
running orders of magnitude faster.
| [
"Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David\n Sontag, Yichen Wu, Michael Zhu",
"['Sanjeev Arora' 'Rong Ge' 'Yoni Halpern' 'David Mimno' 'Ankur Moitra'\n 'David Sontag' 'Yichen Wu' 'Michael Zhu']"
] |
cs.LG cs.SD | null | 1212.5091 | null | null | http://arxiv.org/pdf/1212.5091v1 | 2012-12-19T17:40:07Z | 2012-12-19T17:40:07Z | Maximally Informative Observables and Categorical Perception | We formulate the problem of perception in the framework of information
theory, and prove that categorical perception is equivalent to the existence of
an observable that has the maximum possible information on the target of
perception. We call such an observable maximally informative. Regardless
whether categorical perception is real, maximally informative observables can
form the basis of a theory of perception. We conclude with the implications of
such a theory for the problem of speech perception.
| [
"Elaine Tsiang",
"['Elaine Tsiang']"
] |
cs.LG | null | 1212.5101 | null | null | http://arxiv.org/pdf/1212.5101v1 | 2012-12-20T15:53:43Z | 2012-12-20T15:53:43Z | Hybrid Fuzzy-ART based K-Means Clustering Methodology to Cellular
Manufacturing Using Operational Time | This paper presents a new hybrid Fuzzy-ART based K-Means Clustering technique
to solve the part machine grouping problem in cellular manufacturing systems
considering operational time. The performance of the proposed technique is
tested with problems from open literature and the results are compared to the
existing clustering models such as simple K-means algorithm and modified ART1
algorithm using an efficient modified performance measure known as modified
grouping efficiency (MGE) as found in the literature. The results support the
better performance of the proposed algorithm. The Novelty of this study lies in
the simple and efficient methodology to produce quick solutions for shop floor
managers with least computational efforts and time.
| [
"Sourav Sengupta, Tamal Ghosh, Pranab K Dan, Manojit Chattopadhyay",
"['Sourav Sengupta' 'Tamal Ghosh' 'Pranab K Dan' 'Manojit Chattopadhyay']"
] |
math.ST cs.LG stat.ML stat.TH | 10.1214/14-AOS1218 | 1212.5156 | null | null | http://arxiv.org/abs/1212.5156v3 | 2014-08-28T08:28:48Z | 2012-12-20T17:41:23Z | Nonparametric ridge estimation | We study the problem of estimating the ridges of a density function. Ridge
estimation is an extension of mode finding and is useful for understanding the
structure of a density. It can also be used to find hidden structure in point
cloud data. We show that, under mild regularity conditions, the ridges of the
kernel density estimator consistently estimate the ridges of the true density.
When the data are noisy measurements of a manifold, we show that the ridges are
close and topologically similar to the hidden manifold. To find the estimated
ridges in practice, we adapt the modified mean-shift algorithm proposed by
Ozertem and Erdogmus [J. Mach. Learn. Res. 12 (2011) 1249-1286]. Some numerical
experiments verify that the algorithm is accurate.
| [
"['Christopher R. Genovese' 'Marco Perone-Pacifico' 'Isabella Verdinelli'\n 'Larry Wasserman']",
"Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli,\n Larry Wasserman"
] |
cs.LG cs.CE | null | 1212.5359 | null | null | http://arxiv.org/pdf/1212.5359v1 | 2012-12-21T08:43:05Z | 2012-12-21T08:43:05Z | Fuzzy soft rough K-Means clustering approach for gene expression data | Clustering is one of the widely used data mining techniques for medical
diagnosis. Clustering can be considered as the most important unsupervised
learning technique. Most of the clustering methods group data based on distance
and few methods cluster data based on similarity. The clustering algorithms
classify gene expression data into clusters and the functionally related genes
are grouped together in an efficient manner. The groupings are constructed such
that the degree of relationship is strong among members of the same cluster and
weak among members of different clusters. In this work, we focus on a
similarity relationship among genes with similar expression patterns so that a
consequential and simple analytical decision can be made from the proposed
Fuzzy Soft Rough K-Means algorithm. The algorithm is developed based on Fuzzy
Soft sets and Rough sets. Comparative analysis of the proposed work is made
with bench mark algorithms like K-Means and Rough K-Means and efficiency of the
proposed algorithm is illustrated in this work by using various cluster
validity measures such as DB index and Xie-Beni index.
| [
"['K. Dhanalakshmi' 'H. Hannah Inbarani']",
"K. Dhanalakshmi, H. Hannah Inbarani"
] |
cs.LG cs.CE | null | 1212.5391 | null | null | http://arxiv.org/pdf/1212.5391v1 | 2012-12-21T10:46:24Z | 2012-12-21T10:46:24Z | Soft Set Based Feature Selection Approach for Lung Cancer Images | Lung cancer is the deadliest type of cancer for both men and women. Feature
selection plays a vital role in cancer classification. This paper investigates
the feature selection process in Computed Tomographic (CT) lung cancer images
using soft set theory. We propose a new soft set based unsupervised feature
selection algorithm. Nineteen features are extracted from the segmented lung
images using gray level co-occurence matrix (GLCM) and gray level different
matrix (GLDM). In this paper, an efficient Unsupervised Soft Set based Quick
Reduct (SSUSQR) algorithm is presented. This method is used to select features
from the data set and compared with existing rough set based unsupervised
feature selection methods. Then K-Means and Self Organizing Map (SOM)
clustering algorithms are used to cluster the data. The performance of the
feature selection algorithms is evaluated based on performance of clustering
techniques. The results show that the proposed method effectively removes
redundant features.
| [
"G. Jothi, H. Hannah Inbarani",
"['G. Jothi' 'H. Hannah Inbarani']"
] |
cs.SY cs.LG | 10.1109/TCYB.2014.2343194 | 1212.5524 | null | null | http://arxiv.org/abs/1212.5524v2 | 2013-08-22T16:16:31Z | 2012-12-21T16:57:28Z | Reinforcement learning for port-Hamiltonian systems | Passivity-based control (PBC) for port-Hamiltonian systems provides an
intuitive way of achieving stabilization by rendering a system passive with
respect to a desired storage function. However, in most instances the control
law is obtained without any performance considerations and it has to be
calculated by solving a complex partial differential equation (PDE). In order
to address these issues we introduce a reinforcement learning approach into the
energy-balancing passivity-based control (EB-PBC) method, which is a form of
PBC in which the closed-loop energy is equal to the difference between the
stored and supplied energies. We propose a technique to parameterize EB-PBC
that preserves the systems's PDE matching conditions, does not require the
specification of a global desired Hamiltonian, includes performance criteria,
and is robust to extra non-linearities such as control input saturation. The
parameters of the control law are found using actor-critic reinforcement
learning, enabling learning near-optimal control policies satisfying a desired
closed-loop energy landscape. The advantages are that near-optimal controllers
can be generated using standard energy shaping techniques and that the
solutions learned can be interpreted in terms of energy shaping and damping
injection, which makes it possible to numerically assess stability using
passivity theory. From the reinforcement learning perspective, our proposal
allows for the class of port-Hamiltonian systems to be incorporated in the
actor-critic framework, speeding up the learning thanks to the resulting
parameterization of the policy. The method has been successfully applied to the
pendulum swing-up problem in simulations and real-life experiments.
| [
"['Olivier Sprangers' 'Gabriel A. D. Lopes' 'Robert Babuska']",
"Olivier Sprangers and Gabriel A. D. Lopes and Robert Babuska"
] |
cs.LG stat.ML | null | 1212.5637 | null | null | http://arxiv.org/pdf/1212.5637v1 | 2012-12-21T23:51:21Z | 2012-12-21T23:51:21Z | Random Spanning Trees and the Prediction of Weighted Graphs | We investigate the problem of sequentially predicting the binary labels on
the nodes of an arbitrary weighted graph. We show that, under a suitable
parametrization of the problem, the optimal number of prediction mistakes can
be characterized (up to logarithmic factors) by the cutsize of a random
spanning tree of the graph. The cutsize is induced by the unknown adversarial
labeling of the graph nodes. In deriving our characterization, we obtain a
simple randomized algorithm achieving in expectation the optimal mistake bound
on any polynomially connected weighted graph. Our algorithm draws a random
spanning tree of the original graph and then predicts the nodes of this tree in
constant expected amortized time and linear space. Experiments on real-world
datasets show that our method compares well to both global (Perceptron) and
local (label propagation) methods, while being generally faster in practice.
| [
"Nicolo' Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella",
"[\"Nicolo' Cesa-Bianchi\" 'Claudio Gentile' 'Fabio Vitale'\n 'Giovanni Zappella']"
] |
cs.LG | null | 1212.5701 | null | null | http://arxiv.org/pdf/1212.5701v1 | 2012-12-22T15:46:49Z | 2012-12-22T15:46:49Z | ADADELTA: An Adaptive Learning Rate Method | We present a novel per-dimension learning rate method for gradient descent
called ADADELTA. The method dynamically adapts over time using only first order
information and has minimal computational overhead beyond vanilla stochastic
gradient descent. The method requires no manual tuning of a learning rate and
appears robust to noisy gradient information, different model architecture
choices, various data modalities and selection of hyperparameters. We show
promising results compared to other methods on the MNIST digit classification
task using a single machine and on a large scale voice dataset in a distributed
cluster environment.
| [
"Matthew D. Zeiler",
"['Matthew D. Zeiler']"
] |
cs.LG cs.IT math.IT | 10.1016/j.camwa.2012.12.009 | 1212.5841 | null | null | http://arxiv.org/abs/1212.5841v2 | 2013-01-02T00:00:40Z | 2012-12-23T23:20:14Z | Data complexity measured by principal graphs | How to measure the complexity of a finite set of vectors embedded in a
multidimensional space? This is a non-trivial question which can be approached
in many different ways. Here we suggest a set of data complexity measures using
universal approximators, principal cubic complexes. Principal cubic complexes
generalise the notion of principal manifolds for datasets with non-trivial
topologies. The type of the principal cubic complex is determined by its
dimension and a grammar of elementary graph transformations. The simplest
grammar produces principal trees.
We introduce three natural types of data complexity: 1) geometric (deviation
of the data's approximator from some "idealized" configuration, such as
deviation from harmonicity); 2) structural (how many elements of a principal
graph are needed to approximate the data), and 3) construction complexity (how
many applications of elementary graph transformations are needed to construct
the principal object starting from the simplest one).
We compute these measures for several simulated and real-life data
distributions and show them in the "accuracy-complexity" plots, helping to
optimize the accuracy/complexity ratio. We discuss various issues connected
with measuring data complexity. Software for computing data complexity measures
from principal cubic complexes is provided as well.
| [
"['Andrei Zinovyev' 'Evgeny Mirkes']",
"Andrei Zinovyev and Evgeny Mirkes"
] |
math.ST cs.LG stat.TH | null | 1212.5860 | null | null | http://arxiv.org/pdf/1212.5860v1 | 2012-12-24T03:31:15Z | 2012-12-24T03:31:15Z | A short note on the tail bound of Wishart distribution | We study the tail bound of the emperical covariance of multivariate normal
distribution. Following the work of (Gittens & Tropp, 2011), we provide a tail
bound with a small constant.
| [
"Shenghuo Zhu",
"['Shenghuo Zhu']"
] |
cs.LG cs.NE math.OC stat.ML | null | 1212.5921 | null | null | http://arxiv.org/pdf/1212.5921v1 | 2012-12-24T14:45:25Z | 2012-12-24T14:45:25Z | Distributed optimization of deeply nested systems | In science and engineering, intelligent processing of complex signals such as
images, sound or language is often performed by a parameterized hierarchy of
nonlinear processing layers, sometimes biologically inspired. Hierarchical
systems (or, more generally, nested systems) offer a way to generate complex
mappings using simple stages. Each layer performs a different operation and
achieves an ever more sophisticated representation of the input, as, for
example, in an deep artificial neural network, an object recognition cascade in
computer vision or a speech front-end processing. Joint estimation of the
parameters of all the layers and selection of an optimal architecture is widely
considered to be a difficult numerical nonconvex optimization problem,
difficult to parallelize for execution in a distributed computation
environment, and requiring significant human expert effort, which leads to
suboptimal systems in practice. We describe a general mathematical strategy to
learn the parameters and, to some extent, the architecture of nested systems,
called the method of auxiliary coordinates (MAC). This replaces the original
problem involving a deeply nested function with a constrained problem involving
a different function in an augmented space without nesting. The constrained
problem may be solved with penalty-based methods using alternating optimization
over the parameters and the auxiliary coordinates. MAC has provable
convergence, is easy to implement reusing existing algorithms for single
layers, can be parallelized trivially and massively, applies even when
parameter derivatives are not available or not desirable, and is competitive
with state-of-the-art nonlinear optimizers even in the serial computation
setting, often providing reasonable models within a few iterations.
| [
"Miguel \\'A. Carreira-Perpi\\~n\\'an and Weiran Wang",
"['Miguel Á. Carreira-Perpiñán' 'Weiran Wang']"
] |
q-bio.QM cs.CE cs.LG q-bio.GN stat.AP stat.ML | 10.1093/nar/gkt229 | 1212.5932 | null | null | http://arxiv.org/abs/1212.5932v2 | 2012-12-27T11:23:39Z | 2012-12-24T16:41:08Z | Fully scalable online-preprocessing algorithm for short oligonucleotide
microarray atlases | Accumulation of standardized data collections is opening up novel
opportunities for holistic characterization of genome function. The limited
scalability of current preprocessing techniques has, however, formed a
bottleneck for full utilization of contemporary microarray collections. While
short oligonucleotide arrays constitute a major source of genome-wide profiling
data, scalable probe-level preprocessing algorithms have been available only
for few measurement platforms based on pre-calculated model parameters from
restricted reference training sets. To overcome these key limitations, we
introduce a fully scalable online-learning algorithm that provides tools to
process large microarray atlases including tens of thousands of arrays. Unlike
the alternatives, the proposed algorithm scales up in linear time with respect
to sample size and is readily applicable to all short oligonucleotide
platforms. This is the only available preprocessing algorithm that can learn
probe-level parameters based on sequential hyperparameter updates at small,
consecutive batches of data, thus circumventing the extensive memory
requirements of the standard approaches and opening up novel opportunities to
take full advantage of contemporary microarray data collections. Moreover,
using the most comprehensive data collections to estimate probe-level effects
can assist in pinpointing individual probes affected by various biases and
provide new tools to guide array design and quality control. The implementation
is freely available in R/Bioconductor at
http://www.bioconductor.org/packages/devel/bioc/html/RPA.html
| [
"['Leo Lahti' 'Aurora Torrente' 'Laura L. Elo' 'Alvis Brazma' 'Johan Rung']",
"Leo Lahti, Aurora Torrente, Laura L. Elo, Alvis Brazma, Johan Rung"
] |
stat.ML cs.LG stat.AP | 10.1016/j.patrec.2011.08.019 | 1212.6018 | null | null | http://arxiv.org/abs/1212.6018v1 | 2012-12-25T11:01:48Z | 2012-12-25T11:01:48Z | Exponentially Weighted Moving Average Charts for Detecting Concept Drift | Classifying streaming data requires the development of methods which are
computationally efficient and able to cope with changes in the underlying
distribution of the stream, a phenomenon known in the literature as concept
drift. We propose a new method for detecting concept drift which uses an
Exponentially Weighted Moving Average (EWMA) chart to monitor the
misclassification rate of an streaming classifier. Our approach is modular and
can hence be run in parallel with any underlying classifier to provide an
additional layer of concept drift detection. Moreover our method is
computationally efficient with overhead O(1) and works in a fully online manner
with no need to store data points in memory. Unlike many existing approaches to
concept drift detection, our method allows the rate of false positive
detections to be controlled and kept constant over time.
| [
"Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis, David J. Hand",
"['Gordon J. Ross' 'Niall M. Adams' 'Dimitris K. Tasoulis' 'David J. Hand']"
] |
cs.LG | null | 1212.6031 | null | null | http://arxiv.org/pdf/1212.6031v1 | 2012-12-25T12:12:57Z | 2012-12-25T12:12:57Z | Tangent Bundle Manifold Learning via Grassmann&Stiefel Eigenmaps | One of the ultimate goals of Manifold Learning (ML) is to reconstruct an
unknown nonlinear low-dimensional manifold embedded in a high-dimensional
observation space by a given set of data points from the manifold. We derive a
local lower bound for the maximum reconstruction error in a small neighborhood
of an arbitrary point. The lower bound is defined in terms of the distance
between tangent spaces to the original manifold and the estimated manifold at
the considered point and reconstructed point, respectively. We propose an
amplification of the ML, called Tangent Bundle ML, in which the proximity not
only between the original manifold and its estimator but also between their
tangent spaces is required. We present a new algorithm that solves this problem
and gives a new solution for the ML also.
| [
"Alexander V. Bernstein and Alexander P. Kuleshov",
"['Alexander V. Bernstein' 'Alexander P. Kuleshov']"
] |
cs.LG cs.IR stat.ML | null | 1212.6110 | null | null | http://arxiv.org/pdf/1212.6110v1 | 2012-12-26T02:14:41Z | 2012-12-26T02:14:41Z | Hyperplane Arrangements and Locality-Sensitive Hashing with Lift | Locality-sensitive hashing converts high-dimensional feature vectors, such as
image and speech, into bit arrays and allows high-speed similarity calculation
with the Hamming distance. There is a hashing scheme that maps feature vectors
to bit arrays depending on the signs of the inner products between feature
vectors and the normal vectors of hyperplanes placed in the feature space. This
hashing can be seen as a discretization of the feature space by hyperplanes. If
labels for data are given, one can determine the hyperplanes by using learning
algorithms. However, many proposed learning methods do not consider the
hyperplanes' offsets. Not doing so decreases the number of partitioned regions,
and the correlation between Hamming distances and Euclidean distances becomes
small. In this paper, we propose a lift map that converts learning algorithms
without the offsets to the ones that take into account the offsets. With this
method, the learning methods without the offsets give the discretizations of
spaces as if it takes into account the offsets. For the proposed method, we
input several high-dimensional feature data sets and studied the relationship
between the statistical characteristics of data, the number of hyperplanes, and
the effect of the proposed method.
| [
"Makiko Konoshima and Yui Noma",
"['Makiko Konoshima' 'Yui Noma']"
] |
cs.LG cs.CE | null | 1212.6167 | null | null | http://arxiv.org/pdf/1212.6167v1 | 2012-12-26T12:03:26Z | 2012-12-26T12:03:26Z | Transfer Learning Using Logistic Regression in Credit Scoring | The credit scoring risk management is a fast growing field due to consumer's
credit requests. Credit requests, of new and existing customers, are often
evaluated by classical discrimination rules based on customers information.
However, these kinds of strategies have serious limits and don't take into
account the characteristics difference between current customers and the future
ones. The aim of this paper is to measure credit worthiness for non customers
borrowers and to model potential risk given a heterogeneous population formed
by borrowers customers of the bank and others who are not. We hold on previous
works done in generalized gaussian discrimination and transpose them into the
logistic model to bring out efficient discrimination rules for non customers'
subpopulation.
Therefore we obtain several simple models of connection between parameters of
both logistic models associated respectively to the two subpopulations. The
German credit data set is selected to experiment and to compare these models.
Experimental results show that the use of links between the two subpopulations
improve the classification accuracy for the new loan applicants.
| [
"['Farid Beninel' 'Waad Bouaguel' 'Ghazi Belmufti']",
"Farid Beninel, Waad Bouaguel, Ghazi Belmufti"
] |
stat.ML cs.LG | null | 1212.6246 | null | null | http://arxiv.org/pdf/1212.6246v1 | 2012-12-26T20:45:48Z | 2012-12-26T20:45:48Z | Gaussian Process Regression with Heteroscedastic or Non-Gaussian
Residuals | Gaussian Process (GP) regression models typically assume that residuals are
Gaussian and have the same variance for all observations. However, applications
with input-dependent noise (heteroscedastic residuals) frequently arise in
practice, as do applications in which the residuals do not have a Gaussian
distribution. In this paper, we propose a GP Regression model with a latent
variable that serves as an additional unobserved covariate for the regression.
This model (which we call GPLC) allows for heteroscedasticity since it allows
the function to have a changing partial derivative with respect to this
unobserved covariate. With a suitable covariance function, our GPLC model can
handle (a) Gaussian residuals with input-dependent variance, or (b)
non-Gaussian residuals with input-dependent variance, or (c) Gaussian residuals
with constant variance. We compare our model, using synthetic datasets, with a
model proposed by Goldberg, Williams and Bishop (1998), which we refer to as
GPLV, which only deals with case (a), as well as a standard GP model which can
handle only case (c). Markov Chain Monte Carlo methods are developed for both
modelsl. Experiments show that when the data is heteroscedastic, both GPLC and
GPLV give better results (smaller mean squared error and negative
log-probability density) than standard GP regression. In addition, when the
residual are Gaussian, our GPLC model is generally nearly as good as GPLV,
while when the residuals are non-Gaussian, our GPLC model is better than GPLV.
| [
"Chunyi Wang and Radford M. Neal",
"['Chunyi Wang' 'Radford M. Neal']"
] |
cs.NE cs.AI cs.LG | 10.1109/CCNC.2013.6488435 | 1212.6276 | null | null | http://arxiv.org/abs/1212.6276v1 | 2012-12-26T22:31:13Z | 2012-12-26T22:31:13Z | Echo State Queueing Network: a new reservoir computing learning tool | In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural circuit) occurs. This approach has grown rapidly due to
its success in solving learning tasks and other computational applications.
Some success was also observed with another recently proposed neural network
designed using Queueing Theory, the Random Neural Network (RandNN). Both
approaches have good properties and identified drawbacks. In this paper, we
propose a new RC model called Echo State Queueing Network (ESQN), where we use
ideas coming from RandNNs for the design of the reservoir. ESQNs consist in
ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The
paper positions ESQNs in the global Machine Learning area, and provides
examples of their use and performances. We show on largely used benchmarks that
ESQNs are very accurate tools, and we illustrate how they compare with standard
ESNs.
| [
"['Sebastián Basterrech' 'Gerardo Rubino']",
"Sebasti\\'an Basterrech and Gerardo Rubino"
] |
stat.ML cs.LG | null | 1212.6316 | null | null | http://arxiv.org/pdf/1212.6316v1 | 2012-12-27T07:07:06Z | 2012-12-27T07:07:06Z | On-line relational SOM for dissimilarity data | In some applications and in order to address real world situations better,
data may be more complex than simple vectors. In some examples, they can be
known through their pairwise dissimilarities only. Several variants of the Self
Organizing Map algorithm were introduced to generalize the original algorithm
to this framework. Whereas median SOM is based on a rough representation of the
prototypes, relational SOM allows representing these prototypes by a virtual
combination of all elements in the data set. However, this latter approach
suffers from two main drawbacks. First, its complexity can be large. Second,
only a batch version of this algorithm has been studied so far and it often
provides results having a bad topographic organization. In this article, an
on-line version of relational SOM is described and justified. The algorithm is
tested on several datasets, including categorical data and graphs, and compared
with the batch version and with other SOM algorithms for non vector data.
| [
"Madalina Olteanu (SAMM), Nathalie Villa-Vialaneix (SAMM), Marie\n Cottrell (SAMM)",
"['Madalina Olteanu' 'Nathalie Villa-Vialaneix' 'Marie Cottrell']"
] |
stat.ML cs.AI cs.LG | null | 1212.6659 | null | null | http://arxiv.org/pdf/1212.6659v1 | 2012-12-29T20:23:48Z | 2012-12-29T20:23:48Z | Focus of Attention for Linear Predictors | We present a method to stop the evaluation of a prediction process when the
result of the full evaluation is obvious. This trait is highly desirable in
prediction tasks where a predictor evaluates all its features for every example
in large datasets. We observe that some examples are easier to classify than
others, a phenomenon which is characterized by the event when most of the
features agree on the class of an example. By stopping the feature evaluation
when encountering an easy- to-classify example, the predictor can achieve
substantial gains in computation. Our method provides a natural attention
mechanism for linear predictors where the predictor concentrates most of its
computation on hard-to-classify examples and quickly discards easy-to-classify
ones. By modifying a linear prediction algorithm such as an SVM or AdaBoost to
include our attentive method we prove that the average number of features
computed is O(sqrt(n log 1/sqrt(delta))) where n is the original number of
features, and delta is the error rate incurred due to early stopping. We
demonstrate the effectiveness of Attentive Prediction on MNIST, Real-sim,
Gisette, and synthetic datasets.
| [
"['Raphael Pelossof' 'Zhiliang Ying']",
"Raphael Pelossof and Zhiliang Ying"
] |
cs.DS cs.AI cs.CC cs.LG stat.ML | null | 1212.6846 | null | null | http://arxiv.org/pdf/1212.6846v2 | 2013-01-10T21:20:45Z | 2012-12-31T09:32:51Z | Maximizing a Nonnegative, Monotone, Submodular Function Constrained to
Matchings | Submodular functions have many applications. Matchings have many
applications. The bitext word alignment problem can be modeled as the problem
of maximizing a nonnegative, monotone, submodular function constrained to
matchings in a complete bipartite graph where each vertex corresponds to a word
in the two input sentences and each edge represents a potential word-to-word
translation. We propose a more general problem of maximizing a nonnegative,
monotone, submodular function defined on the edge set of a complete graph
constrained to matchings; we call this problem the CSM-Matching problem.
CSM-Matching also generalizes the maximum-weight matching problem, which has a
polynomial-time algorithm; however, we show that it is NP-hard to approximate
CSM-Matching within a factor of e/(e-1) by reducing the max k-cover problem to
it. Our main result is a simple, greedy, 3-approximation algorithm for
CSM-Matching. Then we reduce CSM-Matching to maximizing a nonnegative,
monotone, submodular function over two matroids, i.e., CSM-2-Matroids.
CSM-2-Matroids has a (2+epsilon)-approximation algorithm - called LSV2. We show
that we can find a (4+epsilon)-approximate solution to CSM-Matching using LSV2.
We extend this approach to similar problems.
| [
"['Sagar Kale']",
"Sagar Kale"
] |
cs.NE cs.LG | null | 1212.6922 | null | null | http://arxiv.org/pdf/1212.6922v1 | 2012-12-31T16:40:50Z | 2012-12-31T16:40:50Z | Training a Functional Link Neural Network Using an Artificial Bee Colony
for Solving a Classification Problems | Artificial Neural Networks have emerged as an important tool for
classification and have been widely used to classify a non-linear separable
pattern. The most popular artificial neural networks model is a Multilayer
Perceptron (MLP) as it is able to perform classification task with significant
success. However due to the complexity of MLP structure and also problems such
as local minima trapping, over fitting and weight interference have made neural
network training difficult. Thus, the easy way to avoid these problems is to
remove the hidden layers. This paper presents the ability of Functional Link
Neural Network (FLNN) to overcome the complexity structure of MLP by using
single layer architecture and propose an Artificial Bee Colony (ABC)
optimization for training the FLNN. The proposed technique is expected to
provide better learning scheme for a classifier in order to get more accurate
classification result
| [
"['Yana Mazwin Mohmad Hassim' 'Rozaida Ghazali']",
"Yana Mazwin Mohmad Hassim and Rozaida Ghazali"
] |
cs.LG math.OC | null | 1212.6958 | null | null | http://arxiv.org/pdf/1212.6958v1 | 2012-12-31T20:13:23Z | 2012-12-31T20:13:23Z | Fast Solutions to Projective Monotone Linear Complementarity Problems | We present a new interior-point potential-reduction algorithm for solving
monotone linear complementarity problems (LCPs) that have a particular special
structure: their matrix $M\in{\mathbb R}^{n\times n}$ can be decomposed as
$M=\Phi U + \Pi_0$, where the rank of $\Phi$ is $k<n$, and $\Pi_0$ denotes
Euclidean projection onto the nullspace of $\Phi^\top$. We call such LCPs
projective. Our algorithm solves a monotone projective LCP to relative accuracy
$\epsilon$ in $O(\sqrt n \ln(1/\epsilon))$ iterations, with each iteration
requiring $O(nk^2)$ flops. This complexity compares favorably with
interior-point algorithms for general monotone LCPs: these algorithms also
require $O(\sqrt n \ln(1/\epsilon))$ iterations, but each iteration needs to
solve an $n\times n$ system of linear equations, a much higher cost than our
algorithm when $k\ll n$. Our algorithm works even though the solution to a
projective LCP is not restricted to lie in any low-rank subspace.
| [
"['Geoffrey J. Gordon']",
"Geoffrey J. Gordon"
] |
cs.LG stat.ML | null | 1301.0015 | null | null | http://arxiv.org/pdf/1301.0015v1 | 2012-12-31T21:07:21Z | 2012-12-31T21:07:21Z | Bethe Bounds and Approximating the Global Optimum | Inference in general Markov random fields (MRFs) is NP-hard, though
identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with
submodular cost functions is efficiently solvable using graph cuts. Marginal
inference, however, even for this restricted class, is in #P. We prove new
formulations of derivatives of the Bethe free energy, provide bounds on the
derivatives and bracket the locations of stationary points, introducing a new
technique called Bethe bound propagation. Several results apply to pairwise
models whether associative or not. Applying these to discretized
pseudo-marginals in the associative case we present a polynomial time
approximation scheme for global optimization provided the maximum degree is
$O(\log n)$, and discuss several extensions.
| [
"Adrian Weller and Tony Jebara",
"['Adrian Weller' 'Tony Jebara']"
] |
math.OC cs.DC cs.LG cs.SI physics.soc-ph | 10.1016/j.neucom.2012.12.043 | 1301.0047 | null | null | http://arxiv.org/abs/1301.0047v1 | 2013-01-01T02:02:51Z | 2013-01-01T02:02:51Z | On Distributed Online Classification in the Midst of Concept Drifts | In this work, we analyze the generalization ability of distributed online
learning algorithms under stationary and non-stationary environments. We derive
bounds for the excess-risk attained by each node in a connected network of
learners and study the performance advantage that diffusion strategies have
over individual non-cooperative processing. We conduct extensive simulations to
illustrate the results.
| [
"['Zaid J. Towfic' 'Jianshu Chen' 'Ali H. Sayed']",
"Zaid J. Towfic, Jianshu Chen, Ali H. Sayed"
] |
cs.LG cs.DC | null | 1301.0082 | null | null | http://arxiv.org/pdf/1301.0082v1 | 2013-01-01T13:20:27Z | 2013-01-01T13:20:27Z | CloudSVM : Training an SVM Classifier in Cloud Computing Systems | In conventional method, distributed support vector machines (SVM) algorithms
are trained over pre-configured intranet/internet environments to find out an
optimal classifier. These methods are very complicated and costly for large
datasets. Hence, we propose a method that is referred as the Cloud SVM training
mechanism (CloudSVM) in a cloud computing environment with MapReduce technique
for distributed machine learning applications. Accordingly, (i) SVM algorithm
is trained in distributed cloud storage servers that work concurrently; (ii)
merge all support vectors in every trained cloud node; and (iii) iterate these
two steps until the SVM converges to the optimal classifier function. Large
scale data sets are not possible to train using SVM algorithm on a single
computer. The results of this study are important for training of large scale
data sets for machine learning applications. We provided that iterative
training of splitted data set in cloud computing environment using SVM will
converge to a global optimal classifier in finite iteration size.
| [
"F. Ozgur Catak and M. Erdal Balaban",
"['F. Ozgur Catak' 'M. Erdal Balaban']"
] |
cs.LG stat.ML | null | 1301.0104 | null | null | http://arxiv.org/pdf/1301.0104v1 | 2013-01-01T16:25:17Z | 2013-01-01T16:25:17Z | Policy Evaluation with Variance Related Risk Criteria in Markov Decision
Processes | In this paper we extend temporal difference policy evaluation algorithms to
performance criteria that include the variance of the cumulative reward. Such
criteria are useful for risk management, and are important in domains such as
finance and process control. We propose both TD(0) and LSTD(lambda) variants
with linear function approximation, prove their convergence, and demonstrate
their utility in a 4-dimensional continuous state space problem.
| [
"['Aviv Tamar' 'Dotan Di Castro' 'Shie Mannor']",
"Aviv Tamar, Dotan Di Castro, Shie Mannor"
] |
stat.ML cs.LG | null | 1301.0142 | null | null | http://arxiv.org/pdf/1301.0142v1 | 2013-01-01T22:52:22Z | 2013-01-01T22:52:22Z | Semi-Supervised Domain Adaptation with Non-Parametric Copulas | A new framework based on the theory of copulas is proposed to address semi-
supervised domain adaptation problems. The presented method factorizes any
multivariate density into a product of marginal distributions and bivariate
cop- ula functions. Therefore, changes in each of these factors can be detected
and corrected to adapt a density model accross different learning domains.
Impor- tantly, we introduce a novel vine copula model, which allows for this
factorization in a non-parametric manner. Experimental results on regression
problems with real-world data illustrate the efficacy of the proposed approach
when compared to state-of-the-art techniques.
| [
"David Lopez-Paz, Jos\\'e Miguel Hern\\'andez-Lobato, Bernhard\n Sch\\\"olkopf",
"['David Lopez-Paz' 'José Miguel Hernández-Lobato' 'Bernhard Schölkopf']"
] |
cs.LG | null | 1301.0179 | null | null | http://arxiv.org/pdf/1301.0179v1 | 2013-01-02T07:13:19Z | 2013-01-02T07:13:19Z | A Novel Design Specification Distance(DSD) Based K-Mean Clustering
Performace Evluation on Engineering Materials Database | Organizing data into semantically more meaningful is one of the fundamental
modes of understanding and learning. Cluster analysis is a formal study of
methods for understanding and algorithm for learning. K-mean clustering
algorithm is one of the most fundamental and simple clustering algorithms. When
there is no prior knowledge about the distribution of data sets, K-mean is the
first choice for clustering with an initial number of clusters. In this paper a
novel distance metric called Design Specification (DS) distance measure
function is integrated with K-mean clustering algorithm to improve cluster
accuracy. The K-means algorithm with proposed distance measure maximizes the
cluster accuracy to 99.98% at P = 1.525, which is determined through the
iterative procedure. The performance of Design Specification (DS) distance
measure function with K - mean algorithm is compared with the performances of
other standard distance functions such as Euclidian, squared Euclidean, City
Block, and Chebshew similarity measures deployed with K-mean algorithm.The
proposed method is evaluated on the engineering materials database. The
experiments on cluster analysis and the outlier profiling show that these is an
excellent improvement in the performance of the proposed method.
| [
"['Doreswamy' 'K. S. Hemanth']",
"Doreswamy, K. S. Hemanth"
] |
cs.LG stat.ML | null | 1301.0534 | null | null | http://arxiv.org/pdf/1301.0534v2 | 2013-01-17T10:03:03Z | 2013-01-03T19:49:14Z | Follow the Leader If You Can, Hedge If You Must | Follow-the-Leader (FTL) is an intuitive sequential prediction strategy that
guarantees constant regret in the stochastic setting, but has terrible
performance for worst-case data. Other hedging strategies have better
worst-case guarantees but may perform much worse than FTL if the data are not
maximally adversarial. We introduce the FlipFlop algorithm, which is the first
method that provably combines the best of both worlds.
As part of our construction, we develop AdaHedge, which is a new way of
dynamically tuning the learning rate in Hedge without using the doubling trick.
AdaHedge refines a method by Cesa-Bianchi, Mansour and Stoltz (2007), yielding
slightly improved worst-case guarantees. By interleaving AdaHedge and FTL, the
FlipFlop algorithm achieves regret within a constant factor of the FTL regret,
without sacrificing AdaHedge's worst-case guarantees.
AdaHedge and FlipFlop do not need to know the range of the losses in advance;
moreover, unlike earlier methods, both have the intuitive property that the
issued weights are invariant under rescaling and translation of the losses. The
losses are also allowed to be negative, in which case they may be interpreted
as gains.
| [
"['Steven de Rooij' 'Tim van Erven' 'Peter D. Grünwald' 'Wouter M. Koolen']",
"Steven de Rooij, Tim van Erven, Peter D. Gr\\\"unwald, Wouter M. Koolen"
] |
cs.LG cs.RO stat.ML | null | 1301.0551 | null | null | http://arxiv.org/pdf/1301.0551v1 | 2012-12-12T15:55:05Z | 2012-12-12T15:55:05Z | Learning Hierarchical Object Maps Of Non-Stationary Environments with
mobile robots | Building models, or maps, of robot environments is a highly active research
area; however, most existing techniques construct unstructured maps and assume
static environments. In this paper, we present an algorithm for learning object
models of non-stationary objects found in office-type environments. Our
algorithm exploits the fact that many objects found in office environments look
alike (e.g., chairs, recycling bins). It does so through a two-level
hierarchical representation, which links individual objects with generic shape
templates of object classes. We derive an approximate EM algorithm for learning
shape parameters at both levels of the hierarchy, using local occupancy grid
maps for representing shape. Additionally, we develop a Bayesian model
selection algorithm that enables the robot to estimate the total number of
objects and object templates in the environment. Experimental results using a
real robot equipped with a laser range finder indicate that our approach
performs well at learning object-based maps of simple office environments. The
approach outperforms a previously developed non-hierarchical algorithm that
models objects but lacks class templates.
| [
"['Dragomir Anguelov' 'Rahul Biswas' 'Daphne Koller' 'Benson Limketkai'\n 'Sebastian Thrun']",
"Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson Limketkai,\n Sebastian Thrun"
] |
cs.LG stat.ML | null | 1301.0554 | null | null | http://arxiv.org/pdf/1301.0554v1 | 2012-12-12T15:55:17Z | 2012-12-12T15:55:17Z | Tree-dependent Component Analysis | We present a generalization of independent component analysis (ICA), where
instead of looking for a linear transform that makes the data components
independent, we look for a transform that makes the data components well fit by
a tree-structured graphical model. Treating the problem as a semiparametric
statistical problem, we show that the optimal transform is found by minimizing
a contrast function based on mutual information, a function that directly
extends the contrast function used for classical ICA. We provide two
approximations of this contrast function, one using kernel density estimation,
and another using kernel generalized variance. This tree-dependent component
analysis framework leads naturally to an efficient general multivariate density
estimation technique where only bivariate density estimation needs to be
performed.
| [
"Francis R. Bach, Michael I. Jordan",
"['Francis R. Bach' 'Michael I. Jordan']"
] |
cs.LG cs.IR stat.ML | null | 1301.0556 | null | null | http://arxiv.org/pdf/1301.0556v1 | 2012-12-12T15:55:25Z | 2012-12-12T15:55:25Z | Learning with Scope, with Application to Information Extraction and
Classification | In probabilistic approaches to classification and information extraction, one
typically builds a statistical model of words under the assumption that future
data will exhibit the same regularities as the training data. In many data
sets, however, there are scope-limited features whose predictive power is only
applicable to a certain subset of the data. For example, in information
extraction from web pages, word formatting may be indicative of extraction
category in different ways on different web pages. The difficulty with using
such features is capturing and exploiting the new regularities encountered in
previously unseen data. In this paper, we propose a hierarchical probabilistic
model that uses both local/scope-limited features, such as word formatting, and
global features, such as word content. The local regularities are modeled as an
unobserved random parameter which is drawn once for each local data set. This
random parameter is estimated during the inference process and then used to
perform classification with both the local and global features--- a procedure
which is akin to automatically retuning the classifier to the local
regularities on each newly encountered web page. Exact inference is intractable
and we present approximations via point estimates and variational methods.
Empirical results on large collections of web data demonstrate that this method
significantly improves performance from traditional models of global features
alone.
| [
"David Blei, J Andrew Bagnell, Andrew McCallum",
"['David Blei' 'J Andrew Bagnell' 'Andrew McCallum']"
] |
cs.LG stat.ML | null | 1301.0562 | null | null | http://arxiv.org/pdf/1301.0562v1 | 2012-12-12T15:55:50Z | 2012-12-12T15:55:50Z | Continuation Methods for Mixing Heterogenous Sources | A number of modern learning tasks involve estimation from heterogeneous
information sources. This includes classification with labeled and unlabeled
data as well as other problems with analogous structure such as competitive
(game theoretic) problems. The associated estimation problems can be typically
reduced to solving a set of fixed point equations (consistency conditions). We
introduce a general method for combining a preferred information source with
another in this setting by evolving continuous paths of fixed points at
intermediate allocations. We explicitly identify critical points along the
unique paths to either increase the stability of estimation or to ensure a
significant departure from the initial source. The homotopy continuation
approach is guaranteed to terminate at the second source, and involves no
combinatorial effort. We illustrate the power of these ideas both in
classification tasks with labeled and unlabeled data, as well as in the context
of a competitive (min-max) formulation of DNA sequence motif discovery.
| [
"['Adrian Corduneanu' 'Tommi S. Jaakkola']",
"Adrian Corduneanu, Tommi S. Jaakkola"
] |
cs.LG cs.AI stat.ML | null | 1301.0563 | null | null | http://arxiv.org/pdf/1301.0563v1 | 2012-12-12T15:55:54Z | 2012-12-12T15:55:54Z | Interpolating Conditional Density Trees | Joint distributions over many variables are frequently modeled by decomposing
them into products of simpler, lower-dimensional conditional distributions,
such as in sparsely connected Bayesian networks. However, automatically
learning such models can be very computationally expensive when there are many
datapoints and many continuous variables with complex nonlinear relationships,
particularly when no good ways of decomposing the joint distribution are known
a priori. In such situations, previous research has generally focused on the
use of discretization techniques in which each continuous variable has a single
discretization that is used throughout the entire network. \ In this paper, we
present and compare a wide variety of tree-based algorithms for learning and
evaluating conditional density estimates over continuous variables. These trees
can be thought of as discretizations that vary according to the particular
interactions being modeled; however, the density within a given leaf of the
tree need not be assumed constant, and we show that such nonuniform leaf
densities lead to more accurate density estimation. We have developed Bayesian
network structure-learning algorithms that employ these tree-based conditional
density representations, and we show that they can be used to practically learn
complex joint probability models over dozens of continuous variables from
thousands of datapoints. We focus on finding models that are simultaneously
accurate, fast to learn, and fast to evaluate once they are learned.
| [
"Scott Davies, Andrew Moore",
"['Scott Davies' 'Andrew Moore']"
] |
cs.LG stat.ML | null | 1301.0565 | null | null | http://arxiv.org/pdf/1301.0565v1 | 2012-12-12T15:56:02Z | 2012-12-12T15:56:02Z | An Information-Theoretic External Cluster-Validity Measure | In this paper we propose a measure of clustering quality or accuracy that is
appropriate in situations where it is desirable to evaluate a clustering
algorithm by somehow comparing the clusters it produces with ``ground truth'
consisting of classes assigned to the patterns by manual means or some other
means in whose veracity there is confidence. Such measures are refered to as
``external'. Our measure also has the characteristic of allowing clusterings
with different numbers of clusters to be compared in a quantitative and
principled way. Our evaluation scheme quantitatively measures how useful the
cluster labels of the patterns are as predictors of their class labels. In
cases where all clusterings to be compared have the same number of clusters,
the measure is equivalent to the mutual information between the cluster labels
and the class labels. In cases where the numbers of clusters are different,
however, it computes the reduction in the number of bits that would be required
to encode (compress) the class labels if both the encoder and decoder have free
acccess to the cluster labels. To achieve this encoding the estimated
conditional probabilities of the class labels given the cluster labels must
also be encoded. These estimated probabilities can be seen as a model for the
class labels and their associated code length as a model cost.
| [
"Byron E Dom",
"['Byron E Dom']"
] |
cs.LG cs.AI | null | 1301.0567 | null | null | http://arxiv.org/pdf/1301.0567v1 | 2012-12-12T15:56:10Z | 2012-12-12T15:56:10Z | The Thing That We Tried Didn't Work Very Well : Deictic Representation
in Reinforcement Learning | Most reinforcement learning methods operate on propositional representations
of the world state. Such representations are often intractably large and
generalize poorly. Using a deictic representation is believed to be a viable
alternative: they promise generalization while allowing the use of existing
reinforcement-learning methods. Yet, there are few experiments on learning with
deictic representations reported in the literature. In this paper we explore
the effectiveness of two forms of deictic representation and a na\"{i}ve
propositional representation in a simple blocks-world domain. We find,
empirically, that the deictic representations actually worsen learning
performance. We conclude with a discussion of possible causes of these results
and strategies for more effective learning in domains with objects.
| [
"Sarah Finney, Natalia Gardiol, Leslie Pack Kaelbling, Tim Oates",
"['Sarah Finney' 'Natalia Gardiol' 'Leslie Pack Kaelbling' 'Tim Oates']"
] |
cs.LG stat.ML | null | 1301.0578 | null | null | http://arxiv.org/pdf/1301.0578v1 | 2012-12-12T15:56:54Z | 2012-12-12T15:56:54Z | Dimension Correction for Hierarchical Latent Class Models | Model complexity is an important factor to consider when selecting among
graphical models. When all variables are observed, the complexity of a model
can be measured by its standard dimension, i.e. the number of independent
parameters. When hidden variables are present, however, standard dimension
might no longer be appropriate. One should instead use effective dimension
(Geiger et al. 1996). This paper is concerned with the computation of effective
dimension. First we present an upper bound on the effective dimension of a
latent class (LC) model. This bound is tight and its computation is easy. We
then consider a generalization of LC models called hierarchical latent class
(HLC) models (Zhang 2002). We show that the effective dimension of an HLC model
can be obtained from the effective dimensions of some related LC models. We
also demonstrate empirically that using effective dimension in place of
standard dimension improves the quality of models learned from data.
| [
"['Tomas Kocka' 'Nevin Lianwen Zhang']",
"Tomas Kocka, Nevin Lianwen Zhang"
] |
cs.LG stat.ML | null | 1301.0579 | null | null | http://arxiv.org/pdf/1301.0579v1 | 2012-12-12T15:56:58Z | 2012-12-12T15:56:58Z | Almost-everywhere algorithmic stability and generalization error | We explore in some detail the notion of algorithmic stability as a viable
framework for analyzing the generalization error of learning algorithms. We
introduce the new notion of training stability of a learning algorithm and show
that, in a general setting, it is sufficient for good bounds on generalization
error. In the PAC setting, training stability is both necessary and sufficient
for learnability.\ The approach based on training stability makes no reference
to VC dimension or VC entropy. There is no need to prove uniform convergence,
and generalization error is bounded directly via an extended McDiarmid
inequality. As a result it potentially allows us to deal with a broader class
of learning algorithms than Empirical Risk Minimization. \ We also explore the
relationships among VC dimension, generalization error, and various notions of
stability. Several examples of learning algorithms are considered.
| [
"['Samuel Kutin' 'Partha Niyogi']",
"Samuel Kutin, Partha Niyogi"
] |
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