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Surrogate Losses in Passive and Active Learning | math.ST cs.LG stat.ML stat.TH | Active learning is a type of sequential design for supervised machine
learning, in which the learning algorithm sequentially requests the labels of
selected instances from a large pool of unlabeled data points. The objective is
to produce a classifier of relatively low risk, as measured under the 0-1 loss,
ideally using fewer label requests than the number of random labeled data
points sufficient to achieve the same. This work investigates the potential
uses of surrogate loss functions in the context of active learning.
Specifically, it presents an active learning algorithm based on an arbitrary
classification-calibrated surrogate loss function, along with an analysis of
the number of label requests sufficient for the classifier returned by the
algorithm to achieve a given risk under the 0-1 loss. Interestingly, these
results cannot be obtained by simply optimizing the surrogate risk via active
learning to an extent sufficient to provide a guarantee on the 0-1 loss, as is
common practice in the analysis of surrogate losses for passive learning. Some
of the results have additional implications for the use of surrogate losses in
passive learning.
| Steve Hanneke and Liu Yang | 10.1214/19-EJS1635 | 1207.3772 | null | null |
Accuracy Measures for the Comparison of Classifiers | cs.LG | The selection of the best classification algorithm for a given dataset is a
very widespread problem. It is also a complex one, in the sense it requires to
make several important methodological choices. Among them, in this work we
focus on the measure used to assess the classification performance and rank the
algorithms. We present the most popular measures and discuss their properties.
Despite the numerous measures proposed over the years, many of them turn out to
be equivalent in this specific case, to have interpretation problems, or to be
unsuitable for our purpose. Consequently, classic overall success rate or
marginal rates should be preferred for this specific task.
| Vincent Labatut (BIT Lab), Hocine Cherifi (Le2i) | null | 1207.3790 | null | null |
Approximate Message Passing with Consistent Parameter Estimation and
Applications to Sparse Learning | cs.IT cs.LG math.IT | We consider the estimation of an i.i.d. (possibly non-Gaussian) vector $\xbf
\in \R^n$ from measurements $\ybf \in \R^m$ obtained by a general cascade model
consisting of a known linear transform followed by a probabilistic
componentwise (possibly nonlinear) measurement channel. A novel method, called
adaptive generalized approximate message passing (Adaptive GAMP), that enables
joint learning of the statistics of the prior and measurement channel along
with estimation of the unknown vector $\xbf$ is presented. The proposed
algorithm is a generalization of a recently-developed EM-GAMP that uses
expectation-maximization (EM) iterations where the posteriors in the E-steps
are computed via approximate message passing. The methodology can be applied to
a large class of learning problems including the learning of sparse priors in
compressed sensing or identification of linear-nonlinear cascade models in
dynamical systems and neural spiking processes. We prove that for large i.i.d.
Gaussian transform matrices the asymptotic componentwise behavior of the
adaptive GAMP algorithm is predicted by a simple set of scalar state evolution
equations. In addition, we show that when a certain maximum-likelihood
estimation can be performed in each step, the adaptive GAMP method can yield
asymptotically consistent parameter estimates, which implies that the algorithm
achieves a reconstruction quality equivalent to the oracle algorithm that knows
the correct parameter values. Remarkably, this result applies to essentially
arbitrary parametrizations of the unknown distributions, including ones that
are nonlinear and non-Gaussian. The adaptive GAMP methodology thus provides a
systematic, general and computationally efficient method applicable to a large
range of complex linear-nonlinear models with provable guarantees.
| Ulugbek S. Kamilov, Sundeep Rangan, Alyson K. Fletcher, Michael Unser | null | 1207.3859 | null | null |
Ensemble Clustering with Logic Rules | stat.ML cs.LG | In this article, the logic rule ensembles approach to supervised learning is
applied to the unsupervised or semi-supervised clustering. Logic rules which
were obtained by combining simple conjunctive rules are used to partition the
input space and an ensemble of these rules is used to define a similarity
matrix. Similarity partitioning is used to partition the data in an
hierarchical manner. We have used internal and external measures of cluster
validity to evaluate the quality of clusterings or to identify the number of
clusters.
| Deniz Akdemir | null | 1207.3961 | null | null |
A Two-Stage Combined Classifier in Scale Space Texture Classification | cs.CV cs.LG | Textures often show multiscale properties and hence multiscale techniques are
considered useful for texture analysis. Scale-space theory as a biologically
motivated approach may be used to construct multiscale textures. In this paper
various ways are studied to combine features on different scales for texture
classification of small image patches. We use the N-jet of derivatives up to
the second order at different scales to generate distinct pattern
representations (DPR) of feature subsets. Each feature subset in the DPR is
given to a base classifier (BC) of a two-stage combined classifier. The
decisions made by these BCs are combined in two stages over scales and
derivatives. Various combining systems and their significances and differences
are discussed. The learning curves are used to evaluate the performances. We
found for small sample sizes combining classifiers performs significantly
better than combining feature spaces (CFS). It is also shown that combining
classifiers performs better than the support vector machine on CFS in
multiscale texture classification.
| Mehrdad J. Gangeh, Robert P. W. Duin, Bart M. ter Haar Romeny, Mohamed
S. Kamel | null | 1207.4089 | null | null |
The Minimum Information Principle for Discriminative Learning | cs.LG stat.ML | Exponential models of distributions are widely used in machine learning for
classiffication and modelling. It is well known that they can be interpreted as
maximum entropy models under empirical expectation constraints. In this work,
we argue that for classiffication tasks, mutual information is a more suitable
information theoretic measure to be optimized. We show how the principle of
minimum mutual information generalizes that of maximum entropy, and provides a
comprehensive framework for building discriminative classiffiers. A game
theoretic interpretation of our approach is then given, and several
generalization bounds provided. We present iterative algorithms for solving the
minimum information problem and its convex dual, and demonstrate their
performance on various classiffication tasks. The results show that minimum
information classiffiers outperform the corresponding maximum entropy models.
| Amir Globerson, Naftali Tishby | null | 1207.4110 | null | null |
Algebraic Statistics in Model Selection | cs.LG stat.ML | We develop the necessary theory in computational algebraic geometry to place
Bayesian networks into the realm of algebraic statistics. We present an
algebra{statistics dictionary focused on statistical modeling. In particular,
we link the notion of effiective dimension of a Bayesian network with the
notion of algebraic dimension of a variety. We also obtain the independence and
non{independence constraints on the distributions over the observable variables
implied by a Bayesian network with hidden variables, via a generating set of an
ideal of polynomials associated to the network. These results extend previous
work on the subject. Finally, the relevance of these results for model
selection is discussed.
| Luis David Garcia | null | 1207.4112 | null | null |
On-line Prediction with Kernels and the Complexity Approximation
Principle | cs.LG stat.ML | The paper describes an application of Aggregating Algorithm to the problem of
regression. It generalizes earlier results concerned with plain linear
regression to kernel techniques and presents an on-line algorithm which
performs nearly as well as any oblivious kernel predictor. The paper contains
the derivation of an estimate on the performance of this algorithm. The
estimate is then used to derive an application of the Complexity Approximation
Principle to kernel methods.
| Alex Gammerman, Yuri Kalnishkan, Vladimir Vovk | null | 1207.4113 | null | null |
Iterative Conditional Fitting for Gaussian Ancestral Graph Models | stat.ME cs.LG stat.ML | Ancestral graph models, introduced by Richardson and Spirtes (2002),
generalize both Markov random fields and Bayesian networks to a class of graphs
with a global Markov property that is closed under conditioning and
marginalization. By design, ancestral graphs encode precisely the conditional
independence structures that can arise from Bayesian networks with selection
and unobserved (hidden/latent) variables. Thus, ancestral graph models provide
a potentially very useful framework for exploratory model selection when
unobserved variables might be involved in the data-generating process but no
particular hidden structure can be specified. In this paper, we present the
Iterative Conditional Fitting (ICF) algorithm for maximum likelihood estimation
in Gaussian ancestral graph models. The name reflects that in each step of the
procedure a conditional distribution is estimated, subject to constraints,
while a marginal distribution is held fixed. This approach is in duality to the
well-known Iterative Proportional Fitting algorithm, in which marginal
distributions are fitted while conditional distributions are held fixed.
| Mathias Drton, Thomas S. Richardson | null | 1207.4118 | null | null |
Applying Discrete PCA in Data Analysis | cs.LG stat.ML | Methods for analysis of principal components in discrete data have existed
for some time under various names such as grade of membership modelling,
probabilistic latent semantic analysis, and genotype inference with admixture.
In this paper we explore a number of extensions to the common theory, and
present some application of these methods to some common statistical tasks. We
show that these methods can be interpreted as a discrete version of ICA. We
develop a hierarchical version yielding components at different levels of
detail, and additional techniques for Gibbs sampling. We compare the algorithms
on a text prediction task using support vector machines, and to information
retrieval.
| Wray L. Buntine, Aleks Jakulin | null | 1207.4125 | null | null |
Exponential Families for Conditional Random Fields | cs.LG stat.ML | In this paper we de ne conditional random elds in reproducing kernel Hilbert
spaces and show connections to Gaussian Process classi cation. More speci
cally, we prove decomposition results for undirected graphical models and we
give constructions for kernels. Finally we present e cient means of solving the
optimization problem using reduced rank decompositions and we show how
stationarity can be exploited e ciently in the optimization process.
| Yasemin Altun, Alex Smola, Thomas Hofmann | null | 1207.4131 | null | null |
MOB-ESP and other Improvements in Probability Estimation | cs.LG cs.AI stat.ML | A key prerequisite to optimal reasoning under uncertainty in intelligent
systems is to start with good class probability estimates. This paper improves
on the current best probability estimation trees (Bagged-PETs) and also
presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using
several benchmark datasets and multiple metrics. These experiments show that
MOB-ESP outputs significantly more accurate class probabilities than either the
baseline BPETs algorithm or the enhanced version presented here (EB-PETs).
These results are based on metrics closely associated with the average accuracy
of the predictions. MOB-ESP also provides much better probability rankings than
B-PETs. The paper further suggests how these estimation techniques can be
applied in concert with a broader category of classifiers.
| Rodney Nielsen | null | 1207.4132 | null | null |
"Ideal Parent" Structure Learning for Continuous Variable Networks | cs.LG stat.ML | In recent years, there is a growing interest in learning Bayesian networks
with continuous variables. Learning the structure of such networks is a
computationally expensive procedure, which limits most applications to
parameter learning. This problem is even more acute when learning networks with
hidden variables. We present a general method for significantly speeding the
structure search algorithm for continuous variable networks with common
parametric distributions. Importantly, our method facilitates the addition of
new hidden variables into the network structure efficiently. We demonstrate the
method on several data sets, both for learning structure on fully observable
data, and for introducing new hidden variables during structure search.
| Iftach Nachman, Gal Elidan, Nir Friedman | null | 1207.4133 | null | null |
Bayesian Learning in Undirected Graphical Models: Approximate MCMC
algorithms | cs.LG stat.ML | Bayesian learning in undirected graphical models|computing posterior
distributions over parameters and predictive quantities is exceptionally
difficult. We conjecture that for general undirected models, there are no
tractable MCMC (Markov Chain Monte Carlo) schemes giving the correct
equilibrium distribution over parameters. While this intractability, due to the
partition function, is familiar to those performing parameter optimisation,
Bayesian learning of posterior distributions over undirected model parameters
has been unexplored and poses novel challenges. we propose several approximate
MCMC schemes and test on fully observed binary models (Boltzmann machines) for
a small coronary heart disease data set and larger artificial systems. While
approximations must perform well on the model, their interaction with the
sampling scheme is also important. Samplers based on variational mean- field
approximations generally performed poorly, more advanced methods using loopy
propagation, brief sampling and stochastic dynamics lead to acceptable
parameter posteriors. Finally, we demonstrate these techniques on a Markov
random field with hidden variables.
| Iain Murray, Zoubin Ghahramani | null | 1207.4134 | null | null |
Active Model Selection | cs.LG stat.ML | Classical learning assumes the learner is given a labeled data sample, from
which it learns a model. The field of Active Learning deals with the situation
where the learner begins not with a training sample, but instead with resources
that it can use to obtain information to help identify the optimal model. To
better understand this task, this paper presents and analyses the simplified
"(budgeted) active model selection" version, which captures the pure
exploration aspect of many active learning problems in a clean and simple
problem formulation. Here the learner can use a fixed budget of "model probes"
(where each probe evaluates the specified model on a random indistinguishable
instance) to identify which of a given set of possible models has the highest
expected accuracy. Our goal is a policy that sequentially determines which
model to probe next, based on the information observed so far. We present a
formal description of this task, and show that it is NPhard in general. We then
investigate a number of algorithms for this task, including several existing
ones (eg, "Round-Robin", "Interval Estimation", "Gittins") as well as some
novel ones (e.g., "Biased-Robin"), describing first their approximation
properties and then their empirical performance on various problem instances.
We observe empirically that the simple biased-robin algorithm significantly
outperforms the other algorithms in the case of identical costs and priors.
| Omid Madani, Daniel J. Lizotte, Russell Greiner | null | 1207.4138 | null | null |
An Extended Cencov-Campbell Characterization of Conditional Information
Geometry | cs.LG stat.ML | We formulate and prove an axiomatic characterization of conditional
information geometry, for both the normalized and the nonnormalized cases. This
characterization extends the axiomatic derivation of the Fisher geometry by
Cencov and Campbell to the cone of positive conditional models, and as a
special case to the manifold of conditional distributions. Due to the close
connection between the conditional I-divergence and the product Fisher
information metric the characterization provides a new axiomatic interpretation
of the primal problems underlying logistic regression and AdaBoost.
| Guy Lebanon | null | 1207.4139 | null | null |
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector
Time Series | cs.LG stat.ML | We consider the problem of modeling discrete-valued vector time series data
using extensions of Chow-Liu tree models to capture both dependencies across
time and dependencies across variables. Conditional Chow-Liu tree models are
introduced, as an extension to standard Chow-Liu trees, for modeling
conditional rather than joint densities. We describe learning algorithms for
such models and show how they can be used to learn parsimonious representations
for the output distributions in hidden Markov models. These models are applied
to the important problem of simulating and forecasting daily precipitation
occurrence for networks of rain stations. To demonstrate the effectiveness of
the models, we compare their performance versus a number of alternatives using
historical precipitation data from Southwestern Australia and the Western
United States. We illustrate how the structure and parameters of the models can
be used to provide an improved meteorological interpretation of such data.
| Sergey Kirshner, Padhraic Smyth, Andrew Robertson | null | 1207.4142 | null | null |
A Generative Bayesian Model for Aggregating Experts' Probabilities | cs.LG stat.ML | In order to improve forecasts, a decisionmaker often combines probabilities
given by various sources, such as human experts and machine learning
classifiers. When few training data are available, aggregation can be improved
by incorporating prior knowledge about the event being forecasted and about
salient properties of the experts. To this end, we develop a generative
Bayesian aggregation model for probabilistic classi cation. The model includes
an event-specific prior, measures of individual experts' bias, calibration,
accuracy, and a measure of dependence betweeen experts. Rather than require
absolute measures, we show that aggregation may be expressed in terms of
relative accuracy between experts. The model results in a weighted logarithmic
opinion pool (LogOps) that satis es consistency criteria such as the external
Bayesian property. We derive analytic solutions for independent and for
exchangeable experts. Empirical tests demonstrate the model's use, comparing
its accuracy with other aggregation methods.
| Joseph Kahn | null | 1207.4144 | null | null |
A Bayesian Approach toward Active Learning for Collaborative Filtering | cs.LG cs.IR stat.ML | Collaborative filtering is a useful technique for exploiting the preference
patterns of a group of users to predict the utility of items for the active
user. In general, the performance of collaborative filtering depends on the
number of rated examples given by the active user. The more the number of rated
examples given by the active user, the more accurate the predicted ratings will
be. Active learning provides an effective way to acquire the most informative
rated examples from active users. Previous work on active learning for
collaborative filtering only considers the expected loss function based on the
estimated model, which can be misleading when the estimated model is
inaccurate. This paper takes one step further by taking into account of the
posterior distribution of the estimated model, which results in more robust
active learning algorithm. Empirical studies with datasets of movie ratings
show that when the number of ratings from the active user is restricted to be
small, active learning methods only based on the estimated model don't perform
well while the active learning method using the model distribution achieves
substantially better performance.
| Rong Jin, Luo Si | null | 1207.4146 | null | null |
Dynamical Systems Trees | cs.LG stat.ML | We propose dynamical systems trees (DSTs) as a flexible class of models for
describing multiple processes that interact via a hierarchy of aggregating
parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear
dynamical systems to an interactive group scenario. Various individual
processes interact as communities and sub-communities in a tree structure that
is unrolled in time. To accommodate nonlinear temporal activity, each
individual leaf process is modeled as a dynamical system containing discrete
and/or continuous hidden states with discrete and/or Gaussian emissions.
Subsequent higher level parent processes act like hidden Markov models and
mediate the interaction between leaf processes or between other parent
processes in the hierarchy. Aggregator chains are parents of child processes
that they combine and mediate, yielding a compact overall parameterization. We
provide tractable inference and learning algorithms for arbitrary DST
topologies via an efficient structured mean-field algorithm. The diverse
applicability of DSTs is demonstrated by experiments on gene expression data
and by modeling group behavior in the setting of an American football game.
| Andrew Howard, Tony S. Jebara | null | 1207.4148 | null | null |
From Fields to Trees | stat.CO cs.LG | We present new MCMC algorithms for computing the posterior distributions and
expectations of the unknown variables in undirected graphical models with
regular structure. For demonstration purposes, we focus on Markov Random Fields
(MRFs). By partitioning the MRFs into non-overlapping trees, it is possible to
compute the posterior distribution of a particular tree exactly by conditioning
on the remaining tree. These exact solutions allow us to construct efficient
blocked and Rao-Blackwellised MCMC algorithms. We show empirically that tree
sampling is considerably more efficient than other partitioned sampling schemes
and the naive Gibbs sampler, even in cases where loopy belief propagation fails
to converge. We prove that tree sampling exhibits lower variance than the naive
Gibbs sampler and other naive partitioning schemes using the theoretical
measure of maximal correlation. We also construct new information theory tools
for comparing different MCMC schemes and show that, under these, tree sampling
is more efficient.
| Firas Hamze, Nando de Freitas | null | 1207.4149 | null | null |
PAC-learning bounded tree-width Graphical Models | cs.LG cs.DS stat.ML | We show that the class of strongly connected graphical models with treewidth
at most k can be properly efficiently PAC-learnt with respect to the
Kullback-Leibler Divergence. Previous approaches to this problem, such as those
of Chow ([1]), and Ho gen ([7]) have shown that this class is PAC-learnable by
reducing it to a combinatorial optimization problem. However, for k > 1, this
problem is NP-complete ([15]), and so unless P=NP, these approaches will take
exponential amounts of time. Our approach differs significantly from these, in
that it first attempts to find approximate conditional independencies by
solving (polynomially many) submodular optimization problems, and then using a
dynamic programming formulation to combine the approximate conditional
independence information to derive a graphical model with underlying graph of
the tree-width specified. This gives us an efficient (polynomial time in the
number of random variables) PAC-learning algorithm which requires only
polynomial number of samples of the true distribution, and only polynomial
running time.
| Mukund Narasimhan, Jeff A. Bilmes | null | 1207.4151 | null | null |
Maximum Entropy for Collaborative Filtering | cs.IR cs.LG | Within the task of collaborative filtering two challenges for computing
conditional probabilities exist. First, the amount of training data available
is typically sparse with respect to the size of the domain. Thus, support for
higher-order interactions is generally not present. Second, the variables that
we are conditioning upon vary for each query. That is, users label different
variables during each query. For this reason, there is no consistent input to
output mapping. To address these problems we purpose a maximum entropy approach
using a non-standard measure of entropy. This approach can be simplified to
solving a set of linear equations that can be efficiently solved.
| Lawrence Zitnick, Takeo Kanade | null | 1207.4152 | null | null |
Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy
Clustering | cs.LG stat.ML | In this paper, a similarity-driven cluster merging method is proposed for
unsuper-vised fuzzy clustering. The cluster merging method is used to resolve
the problem of cluster validation. Starting with an overspecified number of
clusters in the data, pairs of similar clusters are merged based on the
proposed similarity-driven cluster merging criterion. The similarity between
clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive
threshold is used for merging. In addition, a modified generalized ob- jective
function is used for prototype-based fuzzy clustering. The function includes
the p-norm distance measure as well as principal components of the clusters.
The number of the principal components is determined automatically from the
data being clustered. The properties of this unsupervised fuzzy clustering
algorithm are illustrated by several experiments.
| Xuejian Xiong, Kap Chan, Kian Lee Tan | null | 1207.4155 | null | null |
Graph partition strategies for generalized mean field inference | cs.LG stat.ML | An autonomous variational inference algorithm for arbitrary graphical models
requires the ability to optimize variational approximations over the space of
model parameters as well as over the choice of tractable families used for the
variational approximation. In this paper, we present a novel combination of
graph partitioning algorithms with a generalized mean field (GMF) inference
algorithm. This combination optimizes over disjoint clustering of variables and
performs inference using those clusters. We provide a formal analysis of the
relationship between the graph cut and the GMF approximation, and explore
several graph partition strategies empirically. Our empirical results provide
rather clear support for a weighted version of MinCut as a useful clustering
algorithm for GMF inference, which is consistent with the implications from the
formal analysis.
| Eric P. Xing, Michael I. Jordan, Stuart Russell | null | 1207.4156 | null | null |
An Integrated, Conditional Model of Information Extraction and
Coreference with Applications to Citation Matching | cs.LG cs.DL cs.IR stat.ML | Although information extraction and coreference resolution appear together in
many applications, most current systems perform them as ndependent steps. This
paper describes an approach to integrated inference for extraction and
coreference based on conditionally-trained undirected graphical models. We
discuss the advantages of conditional probability training, and of a
coreference model structure based on graph partitioning. On a data set of
research paper citations, we show significant reduction in error by using
extraction uncertainty to improve coreference citation matching accuracy, and
using coreference to improve the accuracy of the extracted fields.
| Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay | null | 1207.4157 | null | null |
On the Choice of Regions for Generalized Belief Propagation | cs.AI cs.LG | Generalized belief propagation (GBP) has proven to be a promising technique
for approximate inference tasks in AI and machine learning. However, the choice
of a good set of clusters to be used in GBP has remained more of an art then a
science until this day. This paper proposes a sequential approach to adding new
clusters of nodes and their interactions (i.e. "regions") to the approximation.
We first review and analyze the recently introduced region graphs and find that
three kinds of operations ("split", "merge" and "death") leave the free energy
and (under some conditions) the fixed points of GBP invariant. This leads to
the notion of "weakly irreducible" regions as the natural candidates to be
added to the approximation. Computational complexity of the GBP algorithm is
controlled by restricting attention to regions with small "region-width".
Combining the above with an efficient (i.e. local in the graph) measure to
predict the improved accuracy of GBP leads to the sequential "region pursuit"
algorithm for adding new regions bottom-up to the region graph. Experiments
show that this algorithm can indeed perform close to optimally.
| Max Welling | null | 1207.4158 | null | null |
ARMA Time-Series Modeling with Graphical Models | stat.AP cs.LG stat.ME | We express the classic ARMA time-series model as a directed graphical model.
In doing so, we find that the deterministic relationships in the model make it
effectively impossible to use the EM algorithm for learning model parameters.
To remedy this problem, we replace the deterministic relationships with
Gaussian distributions having a small variance, yielding the stochastic ARMA
(ARMA) model. This modification allows us to use the EM algorithm to learn
parmeters and to forecast,even in situations where some data is missing. This
modification, in conjunction with the graphicalmodel approach, also allows us
to include cross predictors in situations where there are multiple times series
and/or additional nontemporal covariates. More surprising,experiments suggest
that the move to stochastic ARMA yields improved accuracy through better
smoothing. We demonstrate improvements afforded by cross prediction and better
smoothing on real data.
| Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher
Meek | null | 1207.4162 | null | null |
Factored Latent Analysis for far-field tracking data | cs.LG stat.ML | This paper uses Factored Latent Analysis (FLA) to learn a factorized,
segmental representation for observations of tracked objects over time.
Factored Latent Analysis is latent class analysis in which the observation
space is subdivided and each aspect of the original space is represented by a
separate latent class model. One could simply treat these factors as completely
independent and ignore their interdependencies or one could concatenate them
together and attempt to learn latent class structure for the complete
observation space. Alternatively, FLA allows the interdependencies to be
exploited in estimating an effective model, which is also capable of
representing a factored latent state. In this paper, FLA is used to learn a set
of factored latent classes to represent different modalities of observations of
tracked objects. Different characteristics of the state of tracked objects are
each represented by separate latent class models, including normalized size,
normalized speed, normalized direction, and position. This model also enables
effective temporal segmentation of these sequences. This method is data-driven,
unsupervised using only pairwise observation statistics. This data-driven and
unsupervised activity classi- fication technique exhibits good performance in
multiple challenging environments.
| Chris Stauffer | null | 1207.4164 | null | null |
Predictive State Representations: A New Theory for Modeling Dynamical
Systems | cs.AI cs.LG | Modeling dynamical systems, both for control purposes and to make predictions
about their behavior, is ubiquitous in science and engineering. Predictive
state representations (PSRs) are a recently introduced class of models for
discrete-time dynamical systems. The key idea behind PSRs and the closely
related OOMs (Jaeger's observable operator models) is to represent the state of
the system as a set of predictions of observable outcomes of experiments one
can do in the system. This makes PSRs rather different from history-based
models such as nth-order Markov models and hidden-state-based models such as
HMMs and POMDPs. We introduce an interesting construct, the systemdynamics
matrix, and show how PSRs can be derived simply from it. We also use this
construct to show formally that PSRs are more general than both nth-order
Markov models and HMMs/POMDPs. Finally, we discuss the main difference between
PSRs and OOMs and conclude with directions for future work.
| Satinder Singh, Michael James, Matthew Rudary | null | 1207.4167 | null | null |
The Author-Topic Model for Authors and Documents | cs.IR cs.LG stat.ML | We introduce the author-topic model, a generative model for documents that
extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include
authorship information. Each author is associated with a multinomial
distribution over topics and each topic is associated with a multinomial
distribution over words. A document with multiple authors is modeled as a
distribution over topics that is a mixture of the distributions associated with
the authors. We apply the model to a collection of 1,700 NIPS conference papers
and 160,000 CiteSeer abstracts. Exact inference is intractable for these
datasets and we use Gibbs sampling to estimate the topic and author
distributions. We compare the performance with two other generative models for
documents, which are special cases of the author-topic model: LDA (a topic
model) and a simple author model in which each author is associated with a
distribution over words rather than a distribution over topics. We show topics
recovered by the author-topic model, and demonstrate applications to computing
similarity between authors and entropy of author output.
| Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth | null | 1207.4169 | null | null |
Variational Chernoff Bounds for Graphical Models | cs.LG stat.ML | Recent research has made significant progress on the problem of bounding log
partition functions for exponential family graphical models. Such bounds have
associated dual parameters that are often used as heuristic estimates of the
marginal probabilities required in inference and learning. However these
variational estimates do not give rigorous bounds on marginal probabilities,
nor do they give estimates for probabilities of more general events than simple
marginals. In this paper we build on this recent work by deriving rigorous
upper and lower bounds on event probabilities for graphical models. Our
approach is based on the use of generalized Chernoff bounds to express bounds
on event probabilities in terms of convex optimization problems; these
optimization problems, in turn, require estimates of generalized log partition
functions. Simulations indicate that this technique can result in useful,
rigorous bounds to complement the heuristic variational estimates, with
comparable computational cost.
| Pradeep Ravikumar, John Lafferty | null | 1207.4172 | null | null |
A Hierarchical Graphical Model for Record Linkage | cs.LG cs.IR stat.ML | The task of matching co-referent records is known among other names as rocord
linkage. For large record-linkage problems, often there is little or no labeled
data available, but unlabeled data shows a reasonable clear structure. For such
problems, unsupervised or semi-supervised methods are preferable to supervised
methods. In this paper, we describe a hierarchical graphical model framework
for the linakge-problem in an unsupervised setting. In addition to proposing
new methods, we also cast existing unsupervised probabilistic record-linkage
methods in this framework. Some of the techniques we propose to minimize
overfitting in the above model are of interest in the general graphical model
setting. We describe a method for incorporating monotinicity constraints in a
graphical model. We also outline a bootstrapping approach of using
"single-field" classifiers to noisily label latent variables in a hierarchical
model. Experimental results show that our proposed unsupervised methods perform
quite competitively even with fully supervised record-linkage methods.
| Pradeep Ravikumar, William Cohen | null | 1207.4180 | null | null |
On the Statistical Efficiency of $\ell_{1,p}$ Multi-Task Learning of
Gaussian Graphical Models | cs.LG stat.ML | In this paper, we present $\ell_{1,p}$ multi-task structure learning for
Gaussian graphical models. We analyze the sufficient number of samples for the
correct recovery of the support union and edge signs. We also analyze the
necessary number of samples for any conceivable method by providing
information-theoretic lower bounds. We compare the statistical efficiency of
multi-task learning versus that of single-task learning. For experiments, we
use a block coordinate descent method that is provably convergent and generates
a sequence of positive definite solutions. We provide experimental validation
on synthetic data as well as on two publicly available real-world data sets,
including functional magnetic resonance imaging and gene expression data.
| Jean Honorio, Tommi Jaakkola and Dimitris Samaras | null | 1207.4255 | null | null |
Better Mixing via Deep Representations | cs.LG | It has previously been hypothesized, and supported with some experimental
evidence, that deeper representations, when well trained, tend to do a better
job at disentangling the underlying factors of variation. We study the
following related conjecture: better representations, in the sense of better
disentangling, can be exploited to produce faster-mixing Markov chains.
Consequently, mixing would be more efficient at higher levels of
representation. To better understand why and how this is happening, we propose
a secondary conjecture: the higher-level samples fill more uniformly the space
they occupy and the high-density manifolds tend to unfold when represented at
higher levels. The paper discusses these hypotheses and tests them
experimentally through visualization and measurements of mixing and
interpolating between samples.
| Yoshua Bengio, Gr\'egoire Mesnil, Yann Dauphin and Salah Rifai | null | 1207.4404 | null | null |
Protein Function Prediction Based on Kernel Logistic Regression with
2-order Graphic Neighbor Information | q-bio.QM cs.LG q-bio.MN | To enhance the accuracy of protein-protein interaction function prediction, a
2-order graphic neighbor information feature extraction method based on
undirected simple graph is proposed in this paper, which extends the 1-order
graphic neighbor featureextraction method. And the chi-square test statistical
method is also involved in feature combination. To demonstrate the
effectiveness of our 2-order graphic neighbor feature, four logistic regression
models (logistic regression (abbrev. LR), diffusion kernel logistic regression
(abbrev. DKLR), polynomial kernel logistic regression (abbrev. PKLR), and
radial basis function (RBF) based kernel logistic regression (abbrev. RBF KLR))
are investigated on the two feature sets. The experimental results of protein
function prediction of Yeast Proteome Database (YPD) using the the
protein-protein interaction data of Munich Information Center for Protein
Sequences (MIPS) show that 2-order graphic neighbor information of proteins can
significantly improve the average overall percentage of protein function
prediction especially with RBF KLR. And, with a new 5-top chi-square feature
combination method, RBF KLR can achieve 99.05% average overall percentage on
2-order neighbor feature combination set.
| Jingwei Liu | null | 1207.4463 | null | null |
Local stability of Belief Propagation algorithm with multiple fixed
points | stat.ML cs.LG | A number of problems in statistical physics and computer science can be
expressed as the computation of marginal probabilities over a Markov random
field. Belief propagation, an iterative message-passing algorithm, computes
exactly such marginals when the underlying graph is a tree. But it has gained
its popularity as an efficient way to approximate them in the more general
case, even if it can exhibits multiple fixed points and is not guaranteed to
converge. In this paper, we express a new sufficient condition for local
stability of a belief propagation fixed point in terms of the graph structure
and the beliefs values at the fixed point. This gives credence to the usual
understanding that Belief Propagation performs better on sparse graphs.
| Victorin Martin, Jean-Marc Lasgouttes and Cyril Furtlehner | 10.3233/978-1-61499-096-3-180 | 1207.4597 | null | null |
Proceedings of the 29th International Conference on Machine Learning
(ICML-12) | cs.LG stat.ML | This is an index to the papers that appear in the Proceedings of the 29th
International Conference on Machine Learning (ICML-12). The conference was held
in Edinburgh, Scotland, June 27th - July 3rd, 2012.
| John Langford and Joelle Pineau (Editors) | null | 1207.4676 | null | null |
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | cs.LG math.OC stat.ML | We propose a randomized block-coordinate variant of the classic Frank-Wolfe
algorithm for convex optimization with block-separable constraints. Despite its
lower iteration cost, we show that it achieves a similar convergence rate in
duality gap as the full Frank-Wolfe algorithm. We also show that, when applied
to the dual structural support vector machine (SVM) objective, this yields an
online algorithm that has the same low iteration complexity as primal
stochastic subgradient methods. However, unlike stochastic subgradient methods,
the block-coordinate Frank-Wolfe algorithm allows us to compute the optimal
step-size and yields a computable duality gap guarantee. Our experiments
indicate that this simple algorithm outperforms competing structural SVM
solvers.
| Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher | null | 1207.4747 | null | null |
Hierarchical Clustering using Randomly Selected Similarities | stat.ML cs.IT cs.LG math.IT | The problem of hierarchical clustering items from pairwise similarities is
found across various scientific disciplines, from biology to networking. Often,
applications of clustering techniques are limited by the cost of obtaining
similarities between pairs of items. While prior work has been developed to
reconstruct clustering using a significantly reduced set of pairwise
similarities via adaptive measurements, these techniques are only applicable
when choice of similarities are available to the user. In this paper, we
examine reconstructing hierarchical clustering under similarity observations
at-random. We derive precise bounds which show that a significant fraction of
the hierarchical clustering can be recovered using fewer than all the pairwise
similarities. We find that the correct hierarchical clustering down to a
constant fraction of the total number of items (i.e., clusters sized O(N)) can
be found using only O(N log N) randomly selected pairwise similarities in
expectation.
| Brian Eriksson | null | 1207.4748 | null | null |
Automorphism Groups of Graphical Models and Lifted Variational Inference | cs.AI cs.LG math.CO stat.CO stat.ML | Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general exponential family. Its group action partitions the
set of random variables and feature functions into equivalent classes (called
orbits) having identical marginals and expectations. Then the inference problem
is effectively reduced to that of computing marginals or expectations for each
class, thus avoiding the need to deal with each individual variable or feature.
We demonstrate the usefulness of this general framework in lifting two classes
of variational approximation for MAP inference: local LP relaxation and local
LP relaxation with cycle constraints; the latter yields the first lifted
inference that operate on a bound tighter than local constraints. Initial
experimental results demonstrate that lifted MAP inference with cycle
constraints achieved the state of the art performance, obtaining much better
objective function values than local approximation while remaining relatively
efficient.
| Hung Hai Bui and Tuyen N. Huynh and Sebastian Riedel | null | 1207.4814 | null | null |
Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural
Network | cs.RO cs.AI cs.LG cs.NE | The paper presents the electronic design and motion planning of a robot based
on decision making regarding its straight motion and precise turn using
Artificial Neural Network (ANN). The ANN helps in learning of robot so that it
performs motion autonomously. The weights calculated are implemented in
microcontroller. The performance has been tested to be excellent.
| G. N. Tripathi and V. Rihani | null | 1207.4931 | null | null |
Fast nonparametric classification based on data depth | stat.ML cs.LG | A new procedure, called DDa-procedure, is developed to solve the problem of
classifying d-dimensional objects into q >= 2 classes. The procedure is
completely nonparametric; it uses q-dimensional depth plots and a very
efficient algorithm for discrimination analysis in the depth space [0,1]^q.
Specifically, the depth is the zonoid depth, and the algorithm is the
alpha-procedure. In case of more than two classes several binary
classifications are performed and a majority rule is applied. Special
treatments are discussed for 'outsiders', that is, data having zero depth
vector. The DDa-classifier is applied to simulated as well as real data, and
the results are compared with those of similar procedures that have been
recently proposed. In most cases the new procedure has comparable error rates,
but is much faster than other classification approaches, including the SVM.
| Tatjana Lange, Karl Mosler and Pavlo Mozharovskyi | null | 1207.4992 | null | null |
Learning Probabilistic Systems from Tree Samples | cs.LO cs.LG | We consider the problem of learning a non-deterministic probabilistic system
consistent with a given finite set of positive and negative tree samples.
Consistency is defined with respect to strong simulation conformance. We
propose learning algorithms that use traditional and a new "stochastic"
state-space partitioning, the latter resulting in the minimum number of states.
We then use them to solve the problem of "active learning", that uses a
knowledgeable teacher to generate samples as counterexamples to simulation
equivalence queries. We show that the problem is undecidable in general, but
that it becomes decidable under a suitable condition on the teacher which comes
naturally from the way samples are generated from failed simulation checks. The
latter problem is shown to be undecidable if we impose an additional condition
on the learner to always conjecture a "minimum state" hypothesis. We therefore
propose a semi-algorithm using stochastic partitions. Finally, we apply the
proposed (semi-) algorithms to infer intermediate assumptions in an automated
assume-guarantee verification framework for probabilistic systems.
| Anvesh Komuravelli, Corina S. Pasareanu and Edmund M. Clarke | 10.1109/LICS.2012.54 | 1207.5091 | null | null |
Causal Inference on Time Series using Structural Equation Models | stat.ML cs.LG stat.ME | Causal inference uses observations to infer the causal structure of the data
generating system. We study a class of functional models that we call Time
Series Models with Independent Noise (TiMINo). These models require independent
residual time series, whereas traditional methods like Granger causality
exploit the variance of residuals. There are two main contributions: (1)
Theoretical: By restricting the model class (e.g. to additive noise) we can
provide a more general identifiability result than existing ones. This result
incorporates lagged and instantaneous effects that can be nonlinear and do not
need to be faithful, and non-instantaneous feedbacks between the time series.
(2) Practical: If there are no feedback loops between time series, we propose
an algorithm based on non-linear independence tests of time series. When the
data are causally insufficient, or the data generating process does not satisfy
the model assumptions, this algorithm may still give partial results, but
mostly avoids incorrect answers. An extension to (non-instantaneous) feedbacks
is possible, but not discussed. It outperforms existing methods on artificial
and real data. Code can be provided upon request.
| Jonas Peters, Dominik Janzing and Bernhard Sch\"olkopf | null | 1207.5136 | null | null |
Meta-Learning of Exploration/Exploitation Strategies: The Multi-Armed
Bandit Case | cs.AI cs.LG stat.ML | The exploration/exploitation (E/E) dilemma arises naturally in many subfields
of Science. Multi-armed bandit problems formalize this dilemma in its canonical
form. Most current research in this field focuses on generic solutions that can
be applied to a wide range of problems. However, in practice, it is often the
case that a form of prior information is available about the specific class of
target problems. Prior knowledge is rarely used in current solutions due to the
lack of a systematic approach to incorporate it into the E/E strategy.
To address a specific class of E/E problems, we propose to proceed in three
steps: (i) model prior knowledge in the form of a probability distribution over
the target class of E/E problems; (ii) choose a large hypothesis space of
candidate E/E strategies; and (iii), solve an optimization problem to find a
candidate E/E strategy of maximal average performance over a sample of problems
drawn from the prior distribution.
We illustrate this meta-learning approach with two different hypothesis
spaces: one where E/E strategies are numerically parameterized and another
where E/E strategies are represented as small symbolic formulas. We propose
appropriate optimization algorithms for both cases. Our experiments, with
two-armed Bernoulli bandit problems and various playing budgets, show that the
meta-learnt E/E strategies outperform generic strategies of the literature
(UCB1, UCB1-Tuned, UCB-v, KL-UCB and epsilon greedy); they also evaluate the
robustness of the learnt E/E strategies, by tests carried out on arms whose
rewards follow a truncated Gaussian distribution.
| Francis Maes and Damien Ernst and Louis Wehenkel | null | 1207.5208 | null | null |
Optimal discovery with probabilistic expert advice: finite time analysis
and macroscopic optimality | cs.LG stat.ML | We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on the
optimistic paradigm and on the Good-Turing missing mass estimator. We prove two
different regret bounds on the performance of this algorithm under weak
assumptions on the probabilistic experts. Under more restrictive hypotheses, we
also prove a macroscopic optimality result, comparing the algorithm both with
an oracle strategy and with uniform sampling. Finally, we provide numerical
experiments illustrating these theoretical findings.
| Sebastien Bubeck and Damien Ernst and Aurelien Garivier | null | 1207.5259 | null | null |
A Robust Signal Classification Scheme for Cognitive Radio | cs.IT cs.LG cs.NI math.IT | This paper presents a robust signal classification scheme for achieving
comprehensive spectrum sensing of multiple coexisting wireless systems. It is
built upon a group of feature-based signal detection algorithms enhanced by the
proposed dimension cancelation (DIC) method for mitigating the noise
uncertainty problem. The classification scheme is implemented on our testbed
consisting real-world wireless devices. The simulation and experimental
performances agree with each other well and shows the effectiveness and
robustness of the proposed scheme.
| Hanwen Cao and J\"urgen Peissig | null | 1207.5342 | null | null |
Generalization Bounds for Metric and Similarity Learning | cs.LG stat.ML | Recently, metric learning and similarity learning have attracted a large
amount of interest. Many models and optimisation algorithms have been proposed.
However, there is relatively little work on the generalization analysis of such
methods. In this paper, we derive novel generalization bounds of metric and
similarity learning. In particular, we first show that the generalization
analysis reduces to the estimation of the Rademacher average over
"sums-of-i.i.d." sample-blocks related to the specific matrix norm. Then, we
derive generalization bounds for metric/similarity learning with different
matrix-norm regularisers by estimating their specific Rademacher complexities.
Our analysis indicates that sparse metric/similarity learning with $L^1$-norm
regularisation could lead to significantly better bounds than those with
Frobenius-norm regularisation. Our novel generalization analysis develops and
refines the techniques of U-statistics and Rademacher complexity analysis.
| Qiong Cao, Zheng-Chu Guo and Yiming Ying | null | 1207.5437 | null | null |
MCTS Based on Simple Regret | cs.AI cs.LG | UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games
and Markov decision processes, is based on UCB, a sampling policy for the
Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However,
search differs from MAB in that in MCTS it is usually only the final "arm pull"
(the actual move selection) that collects a reward, rather than all "arm
pulls". Therefore, it makes more sense to minimize the simple regret, as
opposed to the cumulative regret. We begin by introducing policies for
multi-armed bandits with lower finite-time and asymptotic simple regret than
UCB, using it to develop a two-stage scheme (SR+CR) for MCTS which outperforms
UCT empirically.
Optimizing the sampling process is itself a metareasoning problem, a solution
of which can use value of information (VOI) techniques. Although the theory of
VOI for search exists, applying it to MCTS is non-trivial, as typical myopic
assumptions fail. Lacking a complete working VOI theory for MCTS, we
nevertheless propose a sampling scheme that is "aware" of VOI, achieving an
algorithm that in empirical evaluation outperforms both UCT and the other
proposed algorithms.
| David Tolpin and Solomon Eyal Shimony | null | 1207.5536 | null | null |
Bellman Error Based Feature Generation using Random Projections on
Sparse Spaces | cs.LG stat.ML | We address the problem of automatic generation of features for value function
approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve
the error of policy evaluation with function approximation, with a convergence
rate similar to that of value iteration. We propose a simple, fast and robust
algorithm based on random projections to generate BEBFs for sparse feature
spaces. We provide a finite sample analysis of the proposed method, and prove
that projections logarithmic in the dimension of the original space are enough
to guarantee contraction in the error. Empirical results demonstrate the
strength of this method.
| Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle
Pineau, Doina Precup | null | 1207.5554 | null | null |
VOI-aware MCTS | cs.AI cs.LG | UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games
and Markov decision processes, is based on UCB1, a sampling policy for the
Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However,
search differs from MAB in that in MCTS it is usually only the final "arm pull"
(the actual move selection) that collects a reward, rather than all "arm
pulls". In this paper, an MCTS sampling policy based on Value of Information
(VOI) estimates of rollouts is suggested. Empirical evaluation of the policy
and comparison to UCB1 and UCT is performed on random MAB instances as well as
on Computer Go.
| David Tolpin and Solomon Eyal Shimony | null | 1207.5589 | null | null |
A New Training Algorithm for Kanerva's Sparse Distributed Memory | cs.CV cs.LG cs.NE | The Sparse Distributed Memory proposed by Pentii Kanerva (SDM in short) was
thought to be a model of human long term memory. The architecture of the SDM
permits to store binary patterns and to retrieve them using partially matching
patterns. However Kanerva's model is especially efficient only in handling
random data. The purpose of this article is to introduce a new approach of
training Kanerva's SDM that can handle efficiently non-random data, and to
provide it the capability to recognize inverted patterns. This approach uses a
signal model which is different from the one proposed for different purposes by
Hely, Willshaw and Hayes in [4]. This article additionally suggests a different
way of creating hard locations in the memory despite the Kanerva's static
model.
| Lou Marvin Caraig | null | 1207.5774 | null | null |
Equivalence of distance-based and RKHS-based statistics in hypothesis
testing | stat.ME cs.LG math.ST stat.ML stat.TH | We provide a unifying framework linking two classes of statistics used in
two-sample and independence testing: on the one hand, the energy distances and
distance covariances from the statistics literature; on the other, maximum mean
discrepancies (MMD), that is, distances between embeddings of distributions to
reproducing kernel Hilbert spaces (RKHS), as established in machine learning.
In the case where the energy distance is computed with a semimetric of negative
type, a positive definite kernel, termed distance kernel, may be defined such
that the MMD corresponds exactly to the energy distance. Conversely, for any
positive definite kernel, we can interpret the MMD as energy distance with
respect to some negative-type semimetric. This equivalence readily extends to
distance covariance using kernels on the product space. We determine the class
of probability distributions for which the test statistics are consistent
against all alternatives. Finally, we investigate the performance of the family
of distance kernels in two-sample and independence tests: we show in particular
that the energy distance most commonly employed in statistics is just one
member of a parametric family of kernels, and that other choices from this
family can yield more powerful tests.
| Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu | 10.1214/13-AOS1140 | 1207.6076 | null | null |
Determinantal point processes for machine learning | stat.ML cs.IR cs.LG | Determinantal point processes (DPPs) are elegant probabilistic models of
repulsion that arise in quantum physics and random matrix theory. In contrast
to traditional structured models like Markov random fields, which become
intractable and hard to approximate in the presence of negative correlations,
DPPs offer efficient and exact algorithms for sampling, marginalization,
conditioning, and other inference tasks. We provide a gentle introduction to
DPPs, focusing on the intuitions, algorithms, and extensions that are most
relevant to the machine learning community, and show how DPPs can be applied to
real-world applications like finding diverse sets of high-quality search
results, building informative summaries by selecting diverse sentences from
documents, modeling non-overlapping human poses in images or video, and
automatically building timelines of important news stories.
| Alex Kulesza, Ben Taskar | 10.1561/2200000044 | 1207.6083 | null | null |
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral
Biometric for Continuous Authentication | cs.CR cs.LG | We investigate whether a classifier can continuously authenticate users based
on the way they interact with the touchscreen of a smart phone. We propose a
set of 30 behavioral touch features that can be extracted from raw touchscreen
logs and demonstrate that different users populate distinct subspaces of this
feature space. In a systematic experiment designed to test how this behavioral
pattern exhibits consistency over time, we collected touch data from users
interacting with a smart phone using basic navigation maneuvers, i.e., up-down
and left-right scrolling. We propose a classification framework that learns the
touch behavior of a user during an enrollment phase and is able to accept or
reject the current user by monitoring interaction with the touch screen. The
classifier achieves a median equal error rate of 0% for intra-session
authentication, 2%-3% for inter-session authentication and below 4% when the
authentication test was carried out one week after the enrollment phase. While
our experimental findings disqualify this method as a standalone authentication
mechanism for long-term authentication, it could be implemented as a means to
extend screen-lock time or as a part of a multi-modal biometric authentication
system.
| Mario Frank, Ralf Biedert, Eugene Ma, Ivan Martinovic, Dawn Song | 10.1109/TIFS.2012.2225048 | 1207.6231 | null | null |
On When and How to use SAT to Mine Frequent Itemsets | cs.AI cs.DB cs.LG | A new stream of research was born in the last decade with the goal of mining
itemsets of interest using Constraint Programming (CP). This has promoted a
natural way to combine complex constraints in a highly flexible manner.
Although CP state-of-the-art solutions formulate the task using Boolean
variables, the few attempts to adopt propositional Satisfiability (SAT)
provided an unsatisfactory performance. This work deepens the study on when and
how to use SAT for the frequent itemset mining (FIM) problem by defining
different encodings with multiple task-driven enumeration options and search
strategies. Although for the majority of the scenarios SAT-based solutions
appear to be non-competitive with CP peers, results show a variety of
interesting cases where SAT encodings are the best option.
| Rui Henriques and In\^es Lynce and Vasco Manquinho | null | 1207.6253 | null | null |
Identifying Users From Their Rating Patterns | cs.IR cs.LG stat.ML | This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track
2) for context-aware movie recommendation systems. The train dataset comprises
4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the
household groupings of a subset of the users. The test dataset comprises 5,450
ratings for which the user label is missing, but the household label is
provided. The challenge required to identify the user labels for the ratings in
the test set. Our main finding is that temporal information (time labels of the
ratings) is significantly more useful for achieving this objective than the
user preferences (the actual ratings). Using a model that leverages on this
fact, we are able to identify users within a known household with an accuracy
of approximately 96% (i.e. misclassification rate around 4%).
| Jos\'e Bento, Nadia Fawaz, Andrea Montanari, Stratis Ioannidis | null | 1207.6379 | null | null |
Optimal Data Collection For Informative Rankings Expose Well-Connected
Graphs | stat.ML cs.LG stat.AP | Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method for collecting data for which the least squares
estimator for the ranking problem has maximal Fisher information. Our approach,
based on experimental design, is to view data collection as a bi-level
optimization problem where the inner problem is the ranking problem and the
outer problem is to identify data which maximizes the informativeness of the
ranking. Under certain assumptions, the data collection problem decouples,
reducing to a problem of finding multigraphs with large algebraic connectivity.
This reduction of the data collection problem to graph-theoretic questions is
one of the primary contributions of this work. As an application, we study the
Yahoo! Movie user rating dataset and demonstrate that the addition of a small
number of well-chosen pairwise comparisons can significantly increase the
Fisher informativeness of the ranking. As another application, we study the
2011-12 NCAA football schedule and propose schedules with the same number of
games which are significantly more informative. Using spectral clustering
methods to identify highly-connected communities within the division, we argue
that the NCAA could improve its notoriously poor rankings by simply scheduling
more out-of-conference games.
| Braxton Osting and Christoph Brune and Stanley J. Osher | null | 1207.6430 | null | null |
Gaussian process regression as a predictive model for Quality-of-Service
in Web service systems | cs.NI cs.LG | In this paper, we present the Gaussian process regression as the predictive
model for Quality-of-Service (QoS) attributes in Web service systems. The goal
is to predict performance of the execution system expressed as QoS attributes
given existing execution system, service repository, and inputs, e.g., streams
of requests. In order to evaluate the performance of Gaussian process
regression the simulation environment was developed. Two quality indexes were
used, namely, Mean Absolute Error and Mean Squared Error. The results obtained
within the experiment show that the Gaussian process performed the best with
linear kernel and statistically significantly better comparing to
Classification and Regression Trees (CART) method.
| Jakub M. Tomczak, Jerzy Swiatek, Krzysztof Latawiec | null | 1207.6910 | null | null |
Supervised Laplacian Eigenmaps with Applications in Clinical Diagnostics
for Pediatric Cardiology | cs.LG | Electronic health records contain rich textual data which possess critical
predictive information for machine-learning based diagnostic aids. However many
traditional machine learning methods fail to simultaneously integrate both
vector space data and text. We present a supervised method using Laplacian
eigenmaps to augment existing machine-learning methods with low-dimensional
representations of textual predictors which preserve the local similarities.
The proposed implementation performs alternating optimization using gradient
descent. For the evaluation we applied our method to over 2,000 patient records
from a large single-center pediatric cardiology practice to predict if patients
were diagnosed with cardiac disease. Our method was compared with latent
semantic indexing, latent Dirichlet allocation, and local Fisher discriminant
analysis. The results were assessed using AUC, MCC, specificity, and
sensitivity. Results indicate supervised Laplacian eigenmaps was the highest
performing method in our study, achieving 0.782 and 0.374 for AUC and MCC
respectively. SLE showed an increase in 8.16% in AUC and 20.6% in MCC over the
baseline which excluded textual data and a 2.69% and 5.35% increase in AUC and
MCC respectively over unsupervised Laplacian eigenmaps. This method allows many
existing machine learning predictors to effectively and efficiently utilize the
potential of textual predictors.
| Thomas Perry and Hongyuan Zha and Patricio Frias and Dadan Zeng and
Mark Braunstein | null | 1207.7035 | null | null |
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant
Inference | cs.LO cs.LG | We address the predicate generation problem in the context of loop invariant
inference. Motivated by the interpolation-based abstraction refinement
technique, we apply the interpolation theorem to synthesize predicates
implicitly implied by program texts. Our technique is able to improve the
effectiveness and efficiency of the learning-based loop invariant inference
algorithm in [14]. We report experiment results of examples from Linux,
SPEC2000, and Tar utility.
| Wonchan Lee (Seoul National University), Yungbum Jung (Seoul National
University), Bow-yaw Wang (Academia Sinica), Kwangkuen Yi (Seoul National
University) | 10.2168/LMCS-8(3:25)2012 | 1207.7167 | null | null |
Learning a peptide-protein binding affinity predictor with kernel ridge
regression | q-bio.QM cs.LG q-bio.BM stat.ML | We propose a specialized string kernel for small bio-molecules, peptides and
pseudo-sequences of binding interfaces. The kernel incorporates
physico-chemical properties of amino acids and elegantly generalize eight
kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the
Radial Basis Function. We provide a low complexity dynamic programming
algorithm for the exact computation of the kernel and a linear time algorithm
for it's approximation. Combined with kernel ridge regression and SupCK, a
novel binding pocket kernel, the proposed kernel yields biologically relevant
and good prediction accuracy on the PepX database. For the first time, a
machine learning predictor is capable of accurately predicting the binding
affinity of any peptide to any protein. The method was also applied to both
single-target and pan-specific Major Histocompatibility Complex class II
benchmark datasets and three Quantitative Structure Affinity Model benchmark
datasets.
On all benchmarks, our method significantly (p-value < 0.057) outperforms the
current state-of-the-art methods at predicting peptide-protein binding
affinities. The proposed approach is flexible and can be applied to predict any
quantitative biological activity. The method should be of value to a large
segment of the research community with the potential to accelerate
peptide-based drug and vaccine development.
| S\'ebastien Gigu\`ere, Mario Marchand, Fran\c{c}ois Laviolette,
Alexandre Drouin and Jacques Corbeil | 10.1186/1471-2105-14-82 | 1207.7253 | null | null |
Oracle inequalities for computationally adaptive model selection | stat.ML cs.LG | We analyze general model selection procedures using penalized empirical loss
minimization under computational constraints. While classical model selection
approaches do not consider computational aspects of performing model selection,
we argue that any practical model selection procedure must not only trade off
estimation and approximation error, but also the computational effort required
to compute empirical minimizers for different function classes. We provide a
framework for analyzing such problems, and we give algorithms for model
selection under a computational budget. These algorithms satisfy oracle
inequalities that show that the risk of the selected model is not much worse
than if we had devoted all of our omputational budget to the optimal function
class.
| Alekh Agarwal, Peter L. Bartlett, John C. Duchi | null | 1208.0129 | null | null |
Fast Planar Correlation Clustering for Image Segmentation | cs.CV cs.DS cs.LG stat.ML | We describe a new optimization scheme for finding high-quality correlation
clusterings in planar graphs that uses weighted perfect matching as a
subroutine. Our method provides lower-bounds on the energy of the optimal
correlation clustering that are typically fast to compute and tight in
practice. We demonstrate our algorithm on the problem of image segmentation
where this approach outperforms existing global optimization techniques in
minimizing the objective and is competitive with the state of the art in
producing high-quality segmentations.
| Julian Yarkony, Alexander T. Ihler, Charless C. Fowlkes | null | 1208.0378 | null | null |
Multidimensional Membership Mixture Models | cs.LG stat.ML | We present the multidimensional membership mixture (M3) models where every
dimension of the membership represents an independent mixture model and each
data point is generated from the selected mixture components jointly. This is
helpful when the data has a certain shared structure. For example, three unique
means and three unique variances can effectively form a Gaussian mixture model
with nine components, while requiring only six parameters to fully describe it.
In this paper, we present three instantiations of M3 models (together with the
learning and inference algorithms): infinite, finite, and hybrid, depending on
whether the number of mixtures is fixed or not. They are built upon Dirichlet
process mixture models, latent Dirichlet allocation, and a combination
respectively. We then consider two applications: topic modeling and learning 3D
object arrangements. Our experiments show that our M3 models achieve better
performance using fewer topics than many classic topic models. We also observe
that topics from the different dimensions of M3 models are meaningful and
orthogonal to each other.
| Yun Jiang, Marcus Lim and Ashutosh Saxena | null | 1208.0402 | null | null |
Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust
Object Instance Recognition | cs.CV cs.LG stat.ML | Motivated by vision tasks such as robust face and object recognition, we
consider the following general problem: given a collection of low-dimensional
linear subspaces in a high-dimensional ambient (image) space, and a query point
(image), efficiently determine the nearest subspace to the query in $\ell^1$
distance. In contrast to the naive exhaustive search which entails large-scale
linear programs, we show that the computational burden can be cut down
significantly by a simple two-stage algorithm: (1) projecting the query and
data-base subspaces into lower-dimensional space by random Cauchy matrix, and
solving small-scale distance evaluations (linear programs) in the projection
space to locate candidate nearest; (2) with few candidates upon independent
repetition of (1), getting back to the high-dimensional space and performing
exhaustive search. To preserve the identity of the nearest subspace with
nontrivial probability, the projection dimension typically is low-order
polynomial of the subspace dimension multiplied by logarithm of number of the
subspaces (Theorem 2.1). The reduced dimensionality and hence complexity
renders the proposed algorithm particularly relevant to vision application such
as robust face and object instance recognition that we investigate empirically.
| Ju Sun and Yuqian Zhang and John Wright | 10.1137/130936166 | 1208.0432 | null | null |
Detection of Deviations in Mobile Applications Network Behavior | cs.CR cs.LG | In this paper a novel system for detecting meaningful deviations in a mobile
application's network behavior is proposed. The main goal of the proposed
system is to protect mobile device users and cellular infrastructure companies
from malicious applications. The new system is capable of: (1) identifying
malicious attacks or masquerading applications installed on a mobile device,
and (2) identifying republishing of popular applications injected with a
malicious code. The detection is performed based on the application's network
traffic patterns only. For each application two types of models are learned.
The first model, local, represents the personal traffic pattern for each user
using an application and is learned on the device. The second model,
collaborative, represents traffic patterns of numerous users using an
application and is learned on the system server. Machine-learning methods are
used for learning and detection purposes. This paper focuses on methods
utilized for local (i.e., on mobile device) learning and detection of
deviations from the normal application's behavior. These methods were
implemented and evaluated on Android devices. The evaluation experiments
demonstrate that: (1) various applications have specific network traffic
patterns and certain application categories can be distinguishable by their
network patterns, (2) different levels of deviations from normal behavior can
be detected accurately, and (3) local learning is feasible and has a low
performance overhead on mobile devices.
| L. Chekina, D. Mimran, L. Rokach, Y. Elovici, B. Shapira | null | 1208.0564 | null | null |
On the Consistency of AUC Pairwise Optimization | cs.LG stat.ML | AUC (area under ROC curve) is an important evaluation criterion, which has
been popularly used in many learning tasks such as class-imbalance learning,
cost-sensitive learning, learning to rank, etc. Many learning approaches try to
optimize AUC, while owing to the non-convexity and discontinuousness of AUC,
almost all approaches work with surrogate loss functions. Thus, the consistency
of AUC is crucial; however, it has been almost untouched before. In this paper,
we provide a sufficient condition for the asymptotic consistency of learning
approaches based on surrogate loss functions. Based on this result, we prove
that exponential loss and logistic loss are consistent with AUC, but hinge loss
is inconsistent. Then, we derive the $q$-norm hinge loss and general hinge loss
that are consistent with AUC. We also derive the consistent bounds for
exponential loss and logistic loss, and obtain the consistent bounds for many
surrogate loss functions under the non-noise setting. Further, we disclose an
equivalence between the exponential surrogate loss of AUC and exponential
surrogate loss of accuracy, and one straightforward consequence of such finding
is that AdaBoost and RankBoost are equivalent.
| Wei Gao and Zhi-Hua Zhou | null | 1208.0645 | null | null |
Wisdom of the Crowd: Incorporating Social Influence in Recommendation
Models | cs.IR cs.LG cs.SI physics.soc-ph | Recommendation systems have received considerable attention recently.
However, most research has been focused on improving the performance of
collaborative filtering (CF) techniques. Social networks, indispensably,
provide us extra information on people's preferences, and should be considered
and deployed to improve the quality of recommendations. In this paper, we
propose two recommendation models, for individuals and for groups respectively,
based on social contagion and social influence network theory. In the
recommendation model for individuals, we improve the result of collaborative
filtering prediction with social contagion outcome, which simulates the result
of information cascade in the decision-making process. In the recommendation
model for groups, we apply social influence network theory to take
interpersonal influence into account to form a settled pattern of disagreement,
and then aggregate opinions of group members. By introducing the concept of
susceptibility and interpersonal influence, the settled rating results are
flexible, and inclined to members whose ratings are "essential".
| Shang Shang, Pan Hui, Sanjeev R. Kulkarni and Paul W. Cuff | null | 1208.0782 | null | null |
A Random Walk Based Model Incorporating Social Information for
Recommendations | cs.IR cs.LG | Collaborative filtering (CF) is one of the most popular approaches to build a
recommendation system. In this paper, we propose a hybrid collaborative
filtering model based on a Makovian random walk to address the data sparsity
and cold start problems in recommendation systems. More precisely, we construct
a directed graph whose nodes consist of items and users, together with item
content, user profile and social network information. We incorporate user's
ratings into edge settings in the graph model. The model provides personalized
recommendations and predictions to individuals and groups. The proposed
algorithms are evaluated on MovieLens and Epinions datasets. Experimental
results show that the proposed methods perform well compared with other
graph-based methods, especially in the cold start case.
| Shang Shang, Sanjeev R. Kulkarni, Paul W. Cuff and Pan Hui | null | 1208.0787 | null | null |
Cross-conformal predictors | stat.ML cs.LG | This note introduces the method of cross-conformal prediction, which is a
hybrid of the methods of inductive conformal prediction and cross-validation,
and studies its validity and predictive efficiency empirically.
| Vladimir Vovk | null | 1208.0806 | null | null |
Learning Theory Approach to Minimum Error Entropy Criterion | cs.LG stat.ML | We consider the minimum error entropy (MEE) criterion and an empirical risk
minimization learning algorithm in a regression setting. A learning theory
approach is presented for this MEE algorithm and explicit error bounds are
provided in terms of the approximation ability and capacity of the involved
hypothesis space when the MEE scaling parameter is large. Novel asymptotic
analysis is conducted for the generalization error associated with Renyi's
entropy and a Parzen window function, to overcome technical difficulties arisen
from the essential differences between the classical least squares problems and
the MEE setting. A semi-norm and the involved symmetrized least squares error
are introduced, which is related to some ranking algorithms.
| Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou | null | 1208.0848 | null | null |
Statistical Results on Filtering and Epi-convergence for Learning-Based
Model Predictive Control | math.OC cs.LG cs.SY | Learning-based model predictive control (LBMPC) is a technique that provides
deterministic guarantees on robustness, while statistical identification tools
are used to identify richer models of the system in order to improve
performance. This technical note provides proofs that elucidate the reasons for
our choice of measurement model, as well as giving proofs concerning the
stochastic convergence of LBMPC. The first part of this note discusses
simultaneous state estimation and statistical identification (or learning) of
unmodeled dynamics, for dynamical systems that can be described by ordinary
differential equations (ODE's). The second part provides proofs concerning the
epi-convergence of different statistical estimators that can be used with the
learning-based model predictive control (LBMPC) technique. In particular, we
prove results on the statistical properties of a nonparametric estimator that
we have designed to have the correct deterministic and stochastic properties
for numerical implementation when used in conjunction with LBMPC.
| Anil Aswani, Humberto Gonzalez, S. Shankar Sastry, Claire Tomlin | null | 1208.0864 | null | null |
Recklessly Approximate Sparse Coding | cs.LG cs.CV stat.ML | It has recently been observed that certain extremely simple feature encoding
techniques are able to achieve state of the art performance on several standard
image classification benchmarks including deep belief networks, convolutional
nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and
several others. Moreover, these "triangle" or "soft threshold" encodings are
ex- tremely efficient to compute. Several intuitive arguments have been put
forward to explain this remarkable performance, yet no mathematical
justification has been offered.
The main result of this report is to show that these features are realized as
an approximate solution to the a non-negative sparse coding problem. Using this
connection we describe several variants of the soft threshold features and
demonstrate their effectiveness on two image classification benchmark tasks.
| Misha Denil and Nando de Freitas | null | 1208.0959 | null | null |
APRIL: Active Preference-learning based Reinforcement Learning | cs.LG | This paper focuses on reinforcement learning (RL) with limited prior
knowledge. In the domain of swarm robotics for instance, the expert can hardly
design a reward function or demonstrate the target behavior, forbidding the use
of both standard RL and inverse reinforcement learning. Although with a limited
expertise, the human expert is still often able to emit preferences and rank
the agent demonstrations. Earlier work has presented an iterative
preference-based RL framework: expert preferences are exploited to learn an
approximate policy return, thus enabling the agent to achieve direct policy
search. Iteratively, the agent selects a new candidate policy and demonstrates
it; the expert ranks the new demonstration comparatively to the previous best
one; the expert's ranking feedback enables the agent to refine the approximate
policy return, and the process is iterated. In this paper, preference-based
reinforcement learning is combined with active ranking in order to decrease the
number of ranking queries to the expert needed to yield a satisfactory policy.
Experiments on the mountain car and the cancer treatment testbeds witness that
a couple of dozen rankings enable to learn a competent policy.
| Riad Akrour (INRIA Saclay - Ile de France, LRI), Marc Schoenauer
(INRIA Saclay - Ile de France, LRI), Mich\`ele Sebag (LRI) | null | 1208.0984 | null | null |
Sequential Estimation Methods from Inclusion Principle | math.ST cs.LG math.PR stat.TH | In this paper, we propose new sequential estimation methods based on
inclusion principle. The main idea is to reformulate the estimation problems as
constructing sequential random intervals and use confidence sequences to
control the associated coverage probabilities. In contrast to existing
asymptotic sequential methods, our estimation procedures rigorously guarantee
the pre-specified levels of confidence.
| Xinjia Chen | null | 1208.1056 | null | null |
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix
Factorization | stat.ML cs.LG math.OC | In this paper, we study the nonnegative matrix factorization problem under
the separability assumption (that is, there exists a cone spanned by a small
subset of the columns of the input nonnegative data matrix containing all
columns), which is equivalent to the hyperspectral unmixing problem under the
linear mixing model and the pure-pixel assumption. We present a family of fast
recursive algorithms, and prove they are robust under any small perturbations
of the input data matrix. This family generalizes several existing
hyperspectral unmixing algorithms and hence provides for the first time a
theoretical justification of their better practical performance.
| Nicolas Gillis and Stephen A. Vavasis | 10.1109/TPAMI.2013.226 | 1208.1237 | null | null |
One Permutation Hashing for Efficient Search and Learning | cs.LG cs.IR cs.IT math.IT stat.CO stat.ML | Recently, the method of b-bit minwise hashing has been applied to large-scale
linear learning and sublinear time near-neighbor search. The major drawback of
minwise hashing is the expensive preprocessing cost, as the method requires
applying (e.g.,) k=200 to 500 permutations on the data. The testing time can
also be expensive if a new data point (e.g., a new document or image) has not
been processed, which might be a significant issue in user-facing applications.
We develop a very simple solution based on one permutation hashing.
Conceptually, given a massive binary data matrix, we permute the columns only
once and divide the permuted columns evenly into k bins; and we simply store,
for each data vector, the smallest nonzero location in each bin. The
interesting probability analysis (which is validated by experiments) reveals
that our one permutation scheme should perform very similarly to the original
(k-permutation) minwise hashing. In fact, the one permutation scheme can be
even slightly more accurate, due to the "sample-without-replacement" effect.
Our experiments with training linear SVM and logistic regression on the
webspam dataset demonstrate that this one permutation hashing scheme can
achieve the same (or even slightly better) accuracies compared to the original
k-permutation scheme. To test the robustness of our method, we also experiment
with the small news20 dataset which is very sparse and has merely on average
500 nonzeros in each data vector. Interestingly, our one permutation scheme
noticeably outperforms the k-permutation scheme when k is not too small on the
news20 dataset. In summary, our method can achieve at least the same accuracy
as the original k-permutation scheme, at merely 1/k of the original
preprocessing cost.
| Ping Li and Art Owen and Cun-Hui Zhang | null | 1208.1259 | null | null |
Data Selection for Semi-Supervised Learning | cs.LG | The real challenge in pattern recognition task and machine learning process
is to train a discriminator using labeled data and use it to distinguish
between future data as accurate as possible. However, most of the problems in
the real world have numerous data, which labeling them is a cumbersome or even
an impossible matter. Semi-supervised learning is one approach to overcome
these types of problems. It uses only a small set of labeled with the company
of huge remain and unlabeled data to train the discriminator. In
semi-supervised learning, it is very essential that which data is labeled and
depend on position of data it effectiveness changes. In this paper, we proposed
an evolutionary approach called Artificial Immune System (AIS) to determine
which data is better to be labeled to get the high quality data. The
experimental results represent the effectiveness of this algorithm in finding
these data points.
| Shafigh Parsazad, Ehsan Saboori and Amin Allahyar | null | 1208.1315 | null | null |
Guess Who Rated This Movie: Identifying Users Through Subspace
Clustering | cs.LG | It is often the case that, within an online recommender system, multiple
users share a common account. Can such shared accounts be identified solely on
the basis of the user- provided ratings? Once a shared account is identified,
can the different users sharing it be identified as well? Whenever such user
identification is feasible, it opens the way to possible improvements in
personalized recommendations, but also raises privacy concerns. We develop a
model for composite accounts based on unions of linear subspaces, and use
subspace clustering for carrying out the identification task. We show that a
significant fraction of such accounts is identifiable in a reliable manner, and
illustrate potential uses for personalized recommendation.
| Amy Zhang, Nadia Fawaz, Stratis Ioannidis and Andrea Montanari | null | 1208.1544 | null | null |
Self-Organizing Time Map: An Abstraction of Temporal Multivariate
Patterns | cs.LG cs.DS | This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators.
| Peter Sarlin | 10.1016/j.neucom.2012.07.011 | 1208.1819 | null | null |
Metric Learning across Heterogeneous Domains by Respectively Aligning
Both Priors and Posteriors | cs.LG | In this paper, we attempts to learn a single metric across two heterogeneous
domains where source domain is fully labeled and has many samples while target
domain has only a few labeled samples but abundant unlabeled samples. To the
best of our knowledge, this task is seldom touched. The proposed learning model
has a simple underlying motivation: all the samples in both the source and the
target domains are mapped into a common space, where both their priors
P(sample)s and their posteriors P(label|sample)s are forced to be respectively
aligned as much as possible. We show that the two mappings, from both the
source domain and the target domain to the common space, can be reparameterized
into a single positive semi-definite(PSD) matrix. Then we develop an efficient
Bregman Projection algorithm to optimize the PDS matrix over which a LogDet
function is used to regularize. Furthermore, we also show that this model can
be easily kernelized and verify its effectiveness in crosslanguage retrieval
task and cross-domain object recognition task.
| Qiang Qian and Songcan Chen | null | 1208.1829 | null | null |
Margin Distribution Controlled Boosting | cs.LG | Schapire's margin theory provides a theoretical explanation to the success of
boosting-type methods and manifests that a good margin distribution (MD) of
training samples is essential for generalization. However the statement that a
MD is good is vague, consequently, many recently developed algorithms try to
generate a MD in their goodness senses for boosting generalization. Unlike
their indirect control over MD, in this paper, we propose an alternative
boosting algorithm termed Margin distribution Controlled Boosting (MCBoost)
which directly controls the MD by introducing and optimizing a key adjustable
margin parameter. MCBoost's optimization implementation adopts the column
generation technique to ensure fast convergence and small number of weak
classifiers involved in the final MCBooster. We empirically demonstrate: 1)
AdaBoost is actually also a MD controlled algorithm and its iteration number
acts as a parameter controlling the distribution and 2) the generalization
performance of MCBoost evaluated on UCI benchmark datasets is validated better
than those of AdaBoost, L2Boost, LPBoost, AdaBoost-CG and MDBoost.
| Guangxu Guo and Songcan Chen | null | 1208.1846 | null | null |
Scaling Multiple-Source Entity Resolution using Statistically Efficient
Transfer Learning | cs.DB cs.LG | We consider a serious, previously-unexplored challenge facing almost all
approaches to scaling up entity resolution (ER) to multiple data sources: the
prohibitive cost of labeling training data for supervised learning of
similarity scores for each pair of sources. While there exists a rich
literature describing almost all aspects of pairwise ER, this new challenge is
arising now due to the unprecedented ability to acquire and store data from
online sources, features driven by ER such as enriched search verticals, and
the uniqueness of noisy and missing data characteristics for each source. We
show on real-world and synthetic data that for state-of-the-art techniques, the
reality of heterogeneous sources means that the number of labeled training data
must scale quadratically in the number of sources, just to maintain constant
precision/recall. We address this challenge with a brand new transfer learning
algorithm which requires far less training data (or equivalently, achieves
superior accuracy with the same data) and is trained using fast convex
optimization. The intuition behind our approach is to adaptively share
structure learned about one scoring problem with all other scoring problems
sharing a data source in common. We demonstrate that our theoretically
motivated approach incurs no runtime cost while it can maintain constant
precision/recall with the cost of labeling increasing only linearly with the
number of sources.
| Sahand Negahban, Benjamin I. P. Rubinstein and Jim Gemmell | null | 1208.1860 | null | null |
Sharp analysis of low-rank kernel matrix approximations | cs.LG math.ST stat.TH | We consider supervised learning problems within the positive-definite kernel
framework, such as kernel ridge regression, kernel logistic regression or the
support vector machine. With kernels leading to infinite-dimensional feature
spaces, a common practical limiting difficulty is the necessity of computing
the kernel matrix, which most frequently leads to algorithms with running time
at least quadratic in the number of observations n, i.e., O(n^2). Low-rank
approximations of the kernel matrix are often considered as they allow the
reduction of running time complexities to O(p^2 n), where p is the rank of the
approximation. The practicality of such methods thus depends on the required
rank p. In this paper, we show that in the context of kernel ridge regression,
for approximations based on a random subset of columns of the original kernel
matrix, the rank p may be chosen to be linear in the degrees of freedom
associated with the problem, a quantity which is classically used in the
statistical analysis of such methods, and is often seen as the implicit number
of parameters of non-parametric estimators. This result enables simple
algorithms that have sub-quadratic running time complexity, but provably
exhibit the same predictive performance than existing algorithms, for any given
problem instance, and not only for worst-case situations.
| Francis Bach (INRIA Paris - Rocquencourt, LIENS) | null | 1208.2015 | null | null |
Inverse Reinforcement Learning with Gaussian Process | cs.LG | We present new algorithms for inverse reinforcement learning (IRL, or inverse
optimal control) in convex optimization settings. We argue that finite-space
IRL can be posed as a convex quadratic program under a Bayesian inference
framework with the objective of maximum a posterior estimation. To deal with
problems in large or even infinite state space, we propose a Gaussian process
model and use preference graphs to represent observations of decision
trajectories. Our method is distinguished from other approaches to IRL in that
it makes no assumptions about the form of the reward function and yet it
retains the promise of computationally manageable implementations for potential
real-world applications. In comparison with an establish algorithm on
small-scale numerical problems, our method demonstrated better accuracy in
apprenticeship learning and a more robust dependence on the number of
observations.
| Qifeng Qiao and Peter A. Beling | null | 1208.2112 | null | null |
Brain tumor MRI image classification with feature selection and
extraction using linear discriminant analysis | cs.CV cs.LG | Feature extraction is a method of capturing visual content of an image. The
feature extraction is the process to represent raw image in its reduced form to
facilitate decision making such as pattern classification. We have tried to
address the problem of classification MRI brain images by creating a robust and
more accurate classifier which can act as an expert assistant to medical
practitioners. The objective of this paper is to present a novel method of
feature selection and extraction. This approach combines the Intensity,
Texture, shape based features and classifies the tumor as white matter, Gray
matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor
contained brain MR images from the Internet Brain Segmentation Repository. The
proposed technique has been carried out over a larger database as compare to
any previous work and is more robust and effective. PCA and Linear Discriminant
Analysis (LDA) were applied on the training sets. The Support Vector Machine
(SVM) classifier served as a comparison of nonlinear techniques Vs linear ones.
PCA and LDA methods are used to reduce the number of features used. The feature
selection using the proposed technique is more beneficial as it analyses the
data according to grouping class variable and gives reduced feature set with
high classification accuracy.
| V. P. Gladis Pushpa Rathi and S. Palani | null | 1208.2128 | null | null |
How to sample if you must: on optimal functional sampling | stat.ML cs.LG | We examine a fundamental problem that models various active sampling setups,
such as network tomography. We analyze sampling of a multivariate normal
distribution with an unknown expectation that needs to be estimated: in our
setup it is possible to sample the distribution from a given set of linear
functionals, and the difficulty addressed is how to optimally select the
combinations to achieve low estimation error. Although this problem is in the
heart of the field of optimal design, no efficient solutions for the case with
many functionals exist. We present some bounds and an efficient sub-optimal
solution for this problem for more structured sets such as binary functionals
that are induced by graph walks.
| Assaf Hallak and Shie Mannor | null | 1208.2417 | null | null |
Path Integral Control by Reproducing Kernel Hilbert Space Embedding | cs.LG stat.ML | We present an embedding of stochastic optimal control problems, of the so
called path integral form, into reproducing kernel Hilbert spaces. Using
consistent, sample based estimates of the embedding leads to a model free,
non-parametric approach for calculation of an approximate solution to the
control problem. This formulation admits a decomposition of the problem into an
invariant and task dependent component. Consequently, we make much more
efficient use of the sample data compared to previous sample based approaches
in this domain, e.g., by allowing sample re-use across tasks. Numerical
examples on test problems, which illustrate the sample efficiency, are
provided.
| Konrad Rawlik and Marc Toussaint and Sethu Vijayakumar | null | 1208.2523 | null | null |
Nonparametric sparsity and regularization | stat.ML cs.LG math.OC | In this work we are interested in the problems of supervised learning and
variable selection when the input-output dependence is described by a nonlinear
function depending on a few variables. Our goal is to consider a sparse
nonparametric model, hence avoiding linear or additive models. The key idea is
to measure the importance of each variable in the model by making use of
partial derivatives. Based on this intuition we propose a new notion of
nonparametric sparsity and a corresponding least squares regularization scheme.
Using concepts and results from the theory of reproducing kernel Hilbert spaces
and proximal methods, we show that the proposed learning algorithm corresponds
to a minimization problem which can be provably solved by an iterative
procedure. The consistency properties of the obtained estimator are studied
both in terms of prediction and selection performance. An extensive empirical
analysis shows that the proposed method performs favorably with respect to the
state-of-the-art methods.
| Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Alessandro
verri | null | 1208.2572 | null | null |
Analysis of a Statistical Hypothesis Based Learning Mechanism for Faster
crawling | cs.LG cs.IR | The growth of world-wide-web (WWW) spreads its wings from an intangible
quantities of web-pages to a gigantic hub of web information which gradually
increases the complexity of crawling process in a search engine. A search
engine handles a lot of queries from various parts of this world, and the
answers of it solely depend on the knowledge that it gathers by means of
crawling. The information sharing becomes a most common habit of the society,
and it is done by means of publishing structured, semi-structured and
unstructured resources on the web. This social practice leads to an exponential
growth of web-resource, and hence it became essential to crawl for continuous
updating of web-knowledge and modification of several existing resources in any
situation. In this paper one statistical hypothesis based learning mechanism is
incorporated for learning the behavior of crawling speed in different
environment of network, and for intelligently control of the speed of crawler.
The scaling technique is used to compare the performance proposed method with
the standard crawler. The high speed performance is observed after scaling, and
the retrieval of relevant web-resource in such a high speed is analyzed.
| Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das | 10.5121/ijaia.2012.3409 | 1208.2808 | null | null |
Detecting Events and Patterns in Large-Scale User Generated Textual
Streams with Statistical Learning Methods | cs.LG cs.CL cs.IR cs.SI stat.AP stat.ML | A vast amount of textual web streams is influenced by events or phenomena
emerging in the real world. The social web forms an excellent modern paradigm,
where unstructured user generated content is published on a regular basis and
in most occasions is freely distributed. The present Ph.D. Thesis deals with
the problem of inferring information - or patterns in general - about events
emerging in real life based on the contents of this textual stream. We show
that it is possible to extract valuable information about social phenomena,
such as an epidemic or even rainfall rates, by automatic analysis of the
content published in Social Media, and in particular Twitter, using Statistical
Machine Learning methods. An important intermediate task regards the formation
and identification of features which characterise a target event; we select and
use those textual features in several linear, non-linear and hybrid inference
approaches achieving a significantly good performance in terms of the applied
loss function. By examining further this rich data set, we also propose methods
for extracting various types of mood signals revealing how affective norms - at
least within the social web's population - evolve during the day and how
significant events emerging in the real world are influencing them. Lastly, we
present some preliminary findings showing several spatiotemporal
characteristics of this textual information as well as the potential of using
it to tackle tasks such as the prediction of voting intentions.
| Vasileios Lampos | null | 1208.2873 | null | null |
Using Program Synthesis for Social Recommendations | cs.LG cs.DB cs.PL cs.SI physics.soc-ph | This paper presents a new approach to select events of interest to a user in
a social media setting where events are generated by the activities of the
user's friends through their mobile devices. We argue that given the unique
requirements of the social media setting, the problem is best viewed as an
inductive learning problem, where the goal is to first generalize from the
users' expressed "likes" and "dislikes" of specific events, then to produce a
program that can be manipulated by the system and distributed to the collection
devices to collect only data of interest. The key contribution of this paper is
a new algorithm that combines existing machine learning techniques with new
program synthesis technology to learn users' preferences. We show that when
compared with the more standard approaches, our new algorithm provides up to
order-of-magnitude reductions in model training time, and significantly higher
prediction accuracies for our target application. The approach also improves on
standard machine learning techniques in that it produces clear programs that
can be manipulated to optimize data collection and filtering.
| Alvin Cheung, Armando Solar-Lezama, Samuel Madden | null | 1208.2925 | null | null |
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis | stat.ML cs.LG | Fisher's linear discriminant analysis (FLDA) is an important dimension
reduction method in statistical pattern recognition. It has been shown that
FLDA is asymptotically Bayes optimal under the homoscedastic Gaussian
assumption. However, this classical result has the following two major
limitations: 1) it holds only for a fixed dimensionality $D$, and thus does not
apply when $D$ and the training sample size $N$ are proportionally large; 2) it
does not provide a quantitative description on how the generalization ability
of FLDA is affected by $D$ and $N$. In this paper, we present an asymptotic
generalization analysis of FLDA based on random matrix theory, in a setting
where both $D$ and $N$ increase and $D/N\longrightarrow\gamma\in[0,1)$. The
obtained lower bound of the generalization discrimination power overcomes both
limitations of the classical result, i.e., it is applicable when $D$ and $N$
are proportionally large and provides a quantitative description of the
generalization ability of FLDA in terms of the ratio $\gamma=D/N$ and the
population discrimination power. Besides, the discrimination power bound also
leads to an upper bound on the generalization error of binary-classification
with FLDA.
| Wei Bian and Dacheng Tao | null | 1208.3030 | null | null |
Metric distances derived from cosine similarity and Pearson and Spearman
correlations | stat.ME cs.LG | We investigate two classes of transformations of cosine similarity and
Pearson and Spearman correlations into metric distances, utilising the simple
tool of metric-preserving functions. The first class puts anti-correlated
objects maximally far apart. Previously known transforms fall within this
class. The second class collates correlated and anti-correlated objects. An
example of such a transformation that yields a metric distance is the sine
function when applied to centered data.
| Stijn van Dongen and Anton J. Enright | null | 1208.3145 | null | null |
Structured Prediction Cascades | stat.ML cs.LG | Structured prediction tasks pose a fundamental trade-off between the need for
model complexity to increase predictive power and the limited computational
resources for inference in the exponentially-sized output spaces such models
require. We formulate and develop the Structured Prediction Cascade
architecture: a sequence of increasingly complex models that progressively
filter the space of possible outputs. The key principle of our approach is that
each model in the cascade is optimized to accurately filter and refine the
structured output state space of the next model, speeding up both learning and
inference in the next layer of the cascade. We learn cascades by optimizing a
novel convex loss function that controls the trade-off between the filtering
efficiency and the accuracy of the cascade, and provide generalization bounds
for both accuracy and efficiency. We also extend our approach to intractable
models using tree-decomposition ensembles, and provide algorithms and theory
for this setting. We evaluate our approach on several large-scale problems,
achieving state-of-the-art performance in handwriting recognition and human
pose recognition. We find that structured prediction cascades allow tremendous
speedups and the use of previously intractable features and models in both
settings.
| David Weiss, Benjamin Sapp, Ben Taskar | null | 1208.3279 | null | null |
Distance Metric Learning for Kernel Machines | stat.ML cs.LG | Recent work in metric learning has significantly improved the
state-of-the-art in k-nearest neighbor classification. Support vector machines
(SVM), particularly with RBF kernels, are amongst the most popular
classification algorithms that uses distance metrics to compare examples. This
paper provides an empirical analysis of the efficacy of three of the most
popular Mahalanobis metric learning algorithms as pre-processing for SVM
training. We show that none of these algorithms generate metrics that lead to
particularly satisfying improvements for SVM-RBF classification. As a remedy we
introduce support vector metric learning (SVML), a novel algorithm that
seamlessly combines the learning of a Mahalanobis metric with the training of
the RBF-SVM parameters. We demonstrate the capabilities of SVML on nine
benchmark data sets of varying sizes and difficulties. In our study, SVML
outperforms all alternative state-of-the-art metric learning algorithms in
terms of accuracy and establishes itself as a serious alternative to the
standard Euclidean metric with model selection by cross validation.
| Zhixiang Xu, Kilian Q. Weinberger, Olivier Chapelle | null | 1208.3422 | null | null |
Efficient Active Learning of Halfspaces: an Aggressive Approach | cs.LG | We study pool-based active learning of half-spaces. We revisit the aggressive
approach for active learning in the realizable case, and show that it can be
made efficient and practical, while also having theoretical guarantees under
reasonable assumptions. We further show, both theoretically and experimentally,
that it can be preferable to mellow approaches. Our efficient aggressive active
learner of half-spaces has formal approximation guarantees that hold when the
pool is separable with a margin. While our analysis is focused on the
realizable setting, we show that a simple heuristic allows using the same
algorithm successfully for pools with low error as well. We further compare the
aggressive approach to the mellow approach, and prove that there are cases in
which the aggressive approach results in significantly better label complexity
compared to the mellow approach. We demonstrate experimentally that substantial
improvements in label complexity can be achieved using the aggressive approach,
for both realizable and low-error settings.
| Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz | null | 1208.3561 | null | null |
An improvement direction for filter selection techniques using
information theory measures and quadratic optimization | cs.LG cs.IT math.IT | Filter selection techniques are known for their simplicity and efficiency.
However this kind of methods doesn't take into consideration the features
inter-redundancy. Consequently the un-removed redundant features remain in the
final classification model, giving lower generalization performance. In this
paper we propose to use a mathematical optimization method that reduces
inter-features redundancy and maximize relevance between each feature and the
target variable.
| Waad Bouaguel and Ghazi Bel Mufti | null | 1208.3689 | null | null |
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