categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
stat.ML cs.LG | null | 1303.6935 | null | null | http://arxiv.org/pdf/1303.6935v1 | 2013-03-27T19:34:05Z | 2013-03-27T19:34:05Z | Efficiently Using Second Order Information in Large l1 Regularization
Problems | We propose a novel general algorithm LHAC that efficiently uses second-order
information to train a class of large-scale l1-regularized problems. Our method
executes cheap iterations while achieving fast local convergence rate by
exploiting the special structure of a low-rank matrix, constructed via
quasi-Newton approximation of the Hessian of the smooth loss function. A greedy
active-set strategy, based on the largest violations in the dual constraints,
is employed to maintain a working set that iteratively estimates the complement
of the optimal active set. This allows for smaller size of subproblems and
eventually identifies the optimal active set. Empirical comparisons confirm
that LHAC is highly competitive with several recently proposed state-of-the-art
specialized solvers for sparse logistic regression and sparse inverse
covariance matrix selection.
| [
"Xiaocheng Tang and Katya Scheinberg",
"['Xiaocheng Tang' 'Katya Scheinberg']"
] |
stat.ML cs.LG | null | 1303.6977 | null | null | http://arxiv.org/pdf/1303.6977v4 | 2013-06-28T11:18:26Z | 2013-03-27T20:51:33Z | ABC Reinforcement Learning | This paper introduces a simple, general framework for likelihood-free
Bayesian reinforcement learning, through Approximate Bayesian Computation
(ABC). The main advantage is that we only require a prior distribution on a
class of simulators (generative models). This is useful in domains where an
analytical probabilistic model of the underlying process is too complex to
formulate, but where detailed simulation models are available. ABC-RL allows
the use of any Bayesian reinforcement learning technique, even in this case. In
addition, it can be seen as an extension of rollout algorithms to the case
where we do not know what the correct model to draw rollouts from is. We
experimentally demonstrate the potential of this approach in a comparison with
LSPI. Finally, we introduce a theorem showing that ABC is a sound methodology
in principle, even when non-sufficient statistics are used.
| [
"Christos Dimitrakakis, Nikolaos Tziortziotis",
"['Christos Dimitrakakis' 'Nikolaos Tziortziotis']"
] |
cs.LG | 10.1109/CVPR.2013.205 | 1303.7043 | null | null | http://arxiv.org/abs/1303.7043v1 | 2013-03-28T05:45:21Z | 2013-03-28T05:45:21Z | Inductive Hashing on Manifolds | Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes that
preserve the Euclidean distance in the original space. Manifold learning
techniques, in contrast, are better able to model the intrinsic structure
embedded in the original high-dimensional data. The complexity of these models,
and the problems with out-of-sample data, have previously rendered them
unsuitable for application to large-scale embedding, however. In this work, we
consider how to learn compact binary embeddings on their intrinsic manifolds.
In order to address the above-mentioned difficulties, we describe an efficient,
inductive solution to the out-of-sample data problem, and a process by which
non-parametric manifold learning may be used as the basis of a hashing method.
Our proposed approach thus allows the development of a range of new hashing
techniques exploiting the flexibility of the wide variety of manifold learning
approaches available. We particularly show that hashing on the basis of t-SNE .
| [
"['Fumin Shen' 'Chunhua Shen' 'Qinfeng Shi' 'Anton van den Hengel'\n 'Zhenmin Tang']",
"Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin\n Tang"
] |
stat.ML cs.LG | 10.1007/978-3-642-39712-7_15 | 1303.7093 | null | null | http://arxiv.org/abs/1303.7093v3 | 2013-04-08T14:26:49Z | 2013-03-28T11:01:53Z | Relevance As a Metric for Evaluating Machine Learning Algorithms | In machine learning, the choice of a learning algorithm that is suitable for
the application domain is critical. The performance metric used to compare
different algorithms must also reflect the concerns of users in the application
domain under consideration. In this work, we propose a novel probability-based
performance metric called Relevance Score for evaluating supervised learning
algorithms. We evaluate the proposed metric through empirical analysis on a
dataset gathered from an intelligent lighting pilot installation. In comparison
to the commonly used Classification Accuracy metric, the Relevance Score proves
to be more appropriate for a certain class of applications.
| [
"['Aravind Kota Gopalakrishna' 'Tanir Ozcelebi' 'Antonio Liotta'\n 'Johan J. Lukkien']",
"Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta, Johan J.\n Lukkien"
] |
math.ST cs.CG cs.LG stat.TH | 10.1214/14-AOS1252 | 1303.7117 | null | null | http://arxiv.org/abs/1303.7117v3 | 2014-11-20T08:16:51Z | 2013-03-28T12:59:00Z | Confidence sets for persistence diagrams | Persistent homology is a method for probing topological properties of point
clouds and functions. The method involves tracking the birth and death of
topological features (2000) as one varies a tuning parameter. Features with
short lifetimes are informally considered to be "topological noise," and those
with a long lifetime are considered to be "topological signal." In this paper,
we bring some statistical ideas to persistent homology. In particular, we
derive confidence sets that allow us to separate topological signal from
topological noise.
| [
"Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry\n Wasserman, Sivaraman Balakrishnan, Aarti Singh",
"['Brittany Terese Fasy' 'Fabrizio Lecci' 'Alessandro Rinaldo'\n 'Larry Wasserman' 'Sivaraman Balakrishnan' 'Aarti Singh']"
] |
cs.SI cs.LG physics.soc-ph stat.ML | null | 1303.7226 | null | null | http://arxiv.org/pdf/1303.7226v1 | 2013-03-28T19:56:39Z | 2013-03-28T19:56:39Z | Detecting Overlapping Temporal Community Structure in Time-Evolving
Networks | We present a principled approach for detecting overlapping temporal community
structure in dynamic networks. Our method is based on the following framework:
find the overlapping temporal community structure that maximizes a quality
function associated with each snapshot of the network subject to a temporal
smoothness constraint. A novel quality function and a smoothness constraint are
proposed to handle overlaps, and a new convex relaxation is used to solve the
resulting combinatorial optimization problem. We provide theoretical guarantees
as well as experimental results that reveal community structure in real and
synthetic networks. Our main insight is that certain structures can be
identified only when temporal correlation is considered and when communities
are allowed to overlap. In general, discovering such overlapping temporal
community structure can enhance our understanding of real-world complex
networks by revealing the underlying stability behind their seemingly chaotic
evolution.
| [
"['Yudong Chen' 'Vikas Kawadia' 'Rahul Urgaonkar']",
"Yudong Chen, Vikas Kawadia, Rahul Urgaonkar"
] |
cs.LG cs.IR cs.SI physics.data-an stat.ML | 10.1145/2487575.2487693 | 1303.7264 | null | null | http://arxiv.org/abs/1303.7264v1 | 2013-03-28T22:34:51Z | 2013-03-28T22:34:51Z | Scalable Text and Link Analysis with Mixed-Topic Link Models | Many data sets contain rich information about objects, as well as pairwise
relations between them. For instance, in networks of websites, scientific
papers, and other documents, each node has content consisting of a collection
of words, as well as hyperlinks or citations to other nodes. In order to
perform inference on such data sets, and make predictions and recommendations,
it is useful to have models that are able to capture the processes which
generate the text at each node and the links between them. In this paper, we
combine classic ideas in topic modeling with a variant of the mixed-membership
block model recently developed in the statistical physics community. The
resulting model has the advantage that its parameters, including the mixture of
topics of each document and the resulting overlapping communities, can be
inferred with a simple and scalable expectation-maximization algorithm. We test
our model on three data sets, performing unsupervised topic classification and
link prediction. For both tasks, our model outperforms several existing
state-of-the-art methods, achieving higher accuracy with significantly less
computation, analyzing a data set with 1.3 million words and 44 thousand links
in a few minutes.
| [
"['Yaojia Zhu' 'Xiaoran Yan' 'Lise Getoor' 'Cristopher Moore']",
"Yaojia Zhu, Xiaoran Yan, Lise Getoor and Cristopher Moore"
] |
cs.IT cs.LG math.IT stat.ML | 10.1109/LSP.2013.2260538 | 1303.7286 | null | null | http://arxiv.org/abs/1303.7286v3 | 2014-01-22T05:35:12Z | 2013-03-29T03:11:21Z | On the symmetrical Kullback-Leibler Jeffreys centroids | Due to the success of the bag-of-word modeling paradigm, clustering
histograms has become an important ingredient of modern information processing.
Clustering histograms can be performed using the celebrated $k$-means
centroid-based algorithm. From the viewpoint of applications, it is usually
required to deal with symmetric distances. In this letter, we consider the
Jeffreys divergence that symmetrizes the Kullback-Leibler divergence, and
investigate the computation of Jeffreys centroids. We first prove that the
Jeffreys centroid can be expressed analytically using the Lambert $W$ function
for positive histograms. We then show how to obtain a fast guaranteed
approximation when dealing with frequency histograms. Finally, we conclude with
some remarks on the $k$-means histogram clustering.
| [
"['Frank Nielsen']",
"Frank Nielsen"
] |
stat.ML cs.LG math.PR | null | 1303.7461 | null | null | http://arxiv.org/pdf/1303.7461v2 | 2014-01-28T21:50:07Z | 2013-03-29T19:15:04Z | Universal Approximation Depth and Errors of Narrow Belief Networks with
Discrete Units | We generalize recent theoretical work on the minimal number of layers of
narrow deep belief networks that can approximate any probability distribution
on the states of their visible units arbitrarily well. We relax the setting of
binary units (Sutskever and Hinton, 2008; Le Roux and Bengio, 2008, 2010;
Mont\'ufar and Ay, 2011) to units with arbitrary finite state spaces, and the
vanishing approximation error to an arbitrary approximation error tolerance.
For example, we show that a $q$-ary deep belief network with $L\geq
2+\frac{q^{\lceil m-\delta \rceil}-1}{q-1}$ layers of width $n \leq m +
\log_q(m) + 1$ for some $m\in \mathbb{N}$ can approximate any probability
distribution on $\{0,1,\ldots,q-1\}^n$ without exceeding a Kullback-Leibler
divergence of $\delta$. Our analysis covers discrete restricted Boltzmann
machines and na\"ive Bayes models as special cases.
| [
"Guido F. Mont\\'ufar",
"['Guido F. Montúfar']"
] |
cs.LG cs.IT math.IT stat.ML | 10.1109/TSP.2014.2333554 | 1303.7474 | null | null | http://arxiv.org/abs/1303.7474v1 | 2013-03-29T19:52:31Z | 2013-03-29T19:52:31Z | Independent Vector Analysis: Identification Conditions and Performance
Bounds | Recently, an extension of independent component analysis (ICA) from one to
multiple datasets, termed independent vector analysis (IVA), has been the
subject of significant research interest. IVA has also been shown to be a
generalization of Hotelling's canonical correlation analysis. In this paper, we
provide the identification conditions for a general IVA formulation, which
accounts for linear, nonlinear, and sample-to-sample dependencies. The
identification conditions are a generalization of previous results for ICA and
for IVA when samples are independently and identically distributed.
Furthermore, a principal aim of IVA is the identification of dependent sources
between datasets. Thus, we provide the additional conditions for when the
arbitrary ordering of the sources within each dataset is common. Performance
bounds in terms of the Cramer-Rao lower bound are also provided for the
demixing matrices and interference to source ratio. The performance of two IVA
algorithms are compared to the theoretical bounds.
| [
"Matthew Anderson, Geng-Shen Fu, Ronald Phlypo, and T\\\"ulay Adal{\\i}",
"['Matthew Anderson' 'Geng-Shen Fu' 'Ronald Phlypo' 'Tülay Adalı']"
] |
cs.CV cs.LG cs.SD | 10.1016/j.sigpro.2013.06 | 1304.0035 | null | null | http://arxiv.org/abs/1304.0035v1 | 2013-03-29T22:00:01Z | 2013-03-29T22:00:01Z | Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals | This paper addresses signal denoising when large-amplitude coefficients form
clusters (groups). The L1-norm and other separable sparsity models do not
capture the tendency of coefficients to cluster (group sparsity). This work
develops an algorithm, called 'overlapping group shrinkage' (OGS), based on the
minimization of a convex cost function involving a group-sparsity promoting
penalty function. The groups are fully overlapping so the denoising method is
translation-invariant and blocking artifacts are avoided. Based on the
principle of majorization-minimization (MM), we derive a simple iterative
minimization algorithm that reduces the cost function monotonically. A
procedure for setting the regularization parameter, based on attenuating the
noise to a specified level, is also described. The proposed approach is
illustrated on speech enhancement, wherein the OGS approach is applied in the
short-time Fourier transform (STFT) domain. The denoised speech produced by OGS
does not suffer from musical noise.
| [
"['Po-Yu Chen' 'Ivan W. Selesnick']",
"Po-Yu Chen and Ivan W. Selesnick"
] |
cs.LG cs.AI cs.GT | null | 1304.0160 | null | null | http://arxiv.org/pdf/1304.0160v8 | 2013-12-25T16:18:54Z | 2013-03-31T06:45:47Z | Parallel Computation Is ESS | There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance.
| [
"Nabarun Mondal and Partha P. Ghosh",
"['Nabarun Mondal' 'Partha P. Ghosh']"
] |
cs.IT cs.LG math.IT math.ST stat.ML stat.TH | 10.1109/TIT.2016.2605122 | 1304.0682 | null | null | http://arxiv.org/abs/1304.0682v8 | 2016-08-25T20:46:55Z | 2013-04-02T16:35:28Z | Sparse Signal Processing with Linear and Nonlinear Observations: A
Unified Shannon-Theoretic Approach | We derive fundamental sample complexity bounds for recovering sparse and
structured signals for linear and nonlinear observation models including sparse
regression, group testing, multivariate regression and problems with missing
features. In general, sparse signal processing problems can be characterized in
terms of the following Markovian property. We are given a set of $N$ variables
$X_1,X_2,\ldots,X_N$, and there is an unknown subset of variables $S \subset
\{1,\ldots,N\}$ that are relevant for predicting outcomes $Y$. More
specifically, when $Y$ is conditioned on $\{X_n\}_{n\in S}$ it is conditionally
independent of the other variables, $\{X_n\}_{n \not \in S}$. Our goal is to
identify the set $S$ from samples of the variables $X$ and the associated
outcomes $Y$. We characterize this problem as a version of the noisy channel
coding problem. Using asymptotic information theoretic analyses, we establish
mutual information formulas that provide sufficient and necessary conditions on
the number of samples required to successfully recover the salient variables.
These mutual information expressions unify conditions for both linear and
nonlinear observations. We then compute sample complexity bounds for the
aforementioned models, based on the mutual information expressions in order to
demonstrate the applicability and flexibility of our results in general sparse
signal processing models.
| [
"['Cem Aksoylar' 'George Atia' 'Venkatesh Saligrama']",
"Cem Aksoylar, George Atia, Venkatesh Saligrama"
] |
cs.LG cs.CV stat.ML | null | 1304.0725 | null | null | http://arxiv.org/pdf/1304.0725v1 | 2013-03-11T05:28:06Z | 2013-03-11T05:28:06Z | Improved Performance of Unsupervised Method by Renovated K-Means | Clustering is a separation of data into groups of similar objects. Every
group called cluster consists of objects that are similar to one another and
dissimilar to objects of other groups. In this paper, the K-Means algorithm is
implemented by three distance functions and to identify the optimal distance
function for clustering methods. The proposed K-Means algorithm is compared
with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means
(DWK-Means) algorithm by using Davis Bouldin index, Execution Time and
Iteration count methods. Experimental results show that the proposed K-Means
algorithm performed better on Iris and Wine dataset when compared with other
three clustering methods.
| [
"['P. Ashok' 'G. M Kadhar Nawaz' 'E. Elayaraja' 'V. Vadivel']",
"P. Ashok, G.M Kadhar Nawaz, E. Elayaraja, V. Vadivel"
] |
cs.LG cs.CC cs.DS | null | 1304.0730 | null | null | http://arxiv.org/pdf/1304.0730v1 | 2013-04-02T18:37:35Z | 2013-04-02T18:37:35Z | Representation, Approximation and Learning of Submodular Functions Using
Low-rank Decision Trees | We study the complexity of approximate representation and learning of
submodular functions over the uniform distribution on the Boolean hypercube
$\{0,1\}^n$. Our main result is the following structural theorem: any
submodular function is $\epsilon$-close in $\ell_2$ to a real-valued decision
tree (DT) of depth $O(1/\epsilon^2)$. This immediately implies that any
submodular function is $\epsilon$-close to a function of at most
$2^{O(1/\epsilon^2)}$ variables and has a spectral $\ell_1$ norm of
$2^{O(1/\epsilon^2)}$. It also implies the closest previous result that states
that submodular functions can be approximated by polynomials of degree
$O(1/\epsilon^2)$ (Cheraghchi et al., 2012). Our result is proved by
constructing an approximation of a submodular function by a DT of rank
$4/\epsilon^2$ and a proof that any rank-$r$ DT can be $\epsilon$-approximated
by a DT of depth $\frac{5}{2}(r+\log(1/\epsilon))$.
We show that these structural results can be exploited to give an
attribute-efficient PAC learning algorithm for submodular functions running in
time $\tilde{O}(n^2) \cdot 2^{O(1/\epsilon^{4})}$. The best previous algorithm
for the problem requires $n^{O(1/\epsilon^{2})}$ time and examples (Cheraghchi
et al., 2012) but works also in the agnostic setting. In addition, we give
improved learning algorithms for a number of related settings.
We also prove that our PAC and agnostic learning algorithms are essentially
optimal via two lower bounds: (1) an information-theoretic lower bound of
$2^{\Omega(1/\epsilon^{2/3})}$ on the complexity of learning monotone
submodular functions in any reasonable model; (2) computational lower bound of
$n^{\Omega(1/\epsilon^{2/3})}$ based on a reduction to learning of sparse
parities with noise, widely-believed to be intractable. These are the first
lower bounds for learning of submodular functions over the uniform
distribution.
| [
"Vitaly Feldman and Pravesh Kothari and Jan Vondrak",
"['Vitaly Feldman' 'Pravesh Kothari' 'Jan Vondrak']"
] |
cs.LG | null | 1304.0740 | null | null | http://arxiv.org/pdf/1304.0740v1 | 2013-04-02T19:11:23Z | 2013-04-02T19:11:23Z | O(logT) Projections for Stochastic Optimization of Smooth and Strongly
Convex Functions | Traditional algorithms for stochastic optimization require projecting the
solution at each iteration into a given domain to ensure its feasibility. When
facing complex domains, such as positive semi-definite cones, the projection
operation can be expensive, leading to a high computational cost per iteration.
In this paper, we present a novel algorithm that aims to reduce the number of
projections for stochastic optimization. The proposed algorithm combines the
strength of several recent developments in stochastic optimization, including
mini-batch, extra-gradient, and epoch gradient descent, in order to effectively
explore the smoothness and strong convexity. We show, both in expectation and
with a high probability, that when the objective function is both smooth and
strongly convex, the proposed algorithm achieves the optimal $O(1/T)$ rate of
convergence with only $O(\log T)$ projections. Our empirical study verifies the
theoretical result.
| [
"Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He",
"['Lijun Zhang' 'Tianbao Yang' 'Rong Jin' 'Xiaofei He']"
] |
cs.CV cs.LG | 10.1109/CVPR.2013.173 | 1304.0840 | null | null | http://arxiv.org/abs/1304.0840v1 | 2013-04-03T04:31:10Z | 2013-04-03T04:31:10Z | A Fast Semidefinite Approach to Solving Binary Quadratic Problems | Many computer vision problems can be formulated as binary quadratic programs
(BQPs). Two classic relaxation methods are widely used for solving BQPs,
namely, spectral methods and semidefinite programming (SDP), each with their
own advantages and disadvantages. Spectral relaxation is simple and easy to
implement, but its bound is loose. Semidefinite relaxation has a tighter bound,
but its computational complexity is high for large scale problems. We present a
new SDP formulation for BQPs, with two desirable properties. First, it has a
similar relaxation bound to conventional SDP formulations. Second, compared
with conventional SDP methods, the new SDP formulation leads to a significantly
more efficient and scalable dual optimization approach, which has the same
degree of complexity as spectral methods. Extensive experiments on various
applications including clustering, image segmentation, co-segmentation and
registration demonstrate the usefulness of our SDP formulation for solving
large-scale BQPs.
| [
"Peng Wang, Chunhua Shen, Anton van den Hengel",
"['Peng Wang' 'Chunhua Shen' 'Anton van den Hengel']"
] |
cs.CV cs.AI cs.LG math.OC stat.ML | null | 1304.1014 | null | null | http://arxiv.org/pdf/1304.1014v2 | 2013-10-13T09:50:26Z | 2013-04-03T17:15:43Z | A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale
SVM Training | Recently, there has been a renewed interest in the machine learning community
for variants of a sparse greedy approximation procedure for concave
optimization known as {the Frank-Wolfe (FW) method}. In particular, this
procedure has been successfully applied to train large-scale instances of
non-linear Support Vector Machines (SVMs). Specializing FW to SVM training has
allowed to obtain efficient algorithms but also important theoretical results,
including convergence analysis of training algorithms and new characterizations
of model sparsity.
In this paper, we present and analyze a novel variant of the FW method based
on a new way to perform away steps, a classic strategy used to accelerate the
convergence of the basic FW procedure. Our formulation and analysis is focused
on a general concave maximization problem on the simplex. However, the
specialization of our algorithm to quadratic forms is strongly related to some
classic methods in computational geometry, namely the Gilbert and MDM
algorithms.
On the theoretical side, we demonstrate that the method matches the
guarantees in terms of convergence rate and number of iterations obtained by
using classic away steps. In particular, the method enjoys a linear rate of
convergence, a result that has been recently proved for MDM on quadratic forms.
On the practical side, we provide experiments on several classification
datasets, and evaluate the results using statistical tests. Experiments show
that our method is faster than the FW method with classic away steps, and works
well even in the cases in which classic away steps slow down the algorithm.
Furthermore, these improvements are obtained without sacrificing the predictive
accuracy of the obtained SVM model.
| [
"Hector Allende, Emanuele Frandi, Ricardo Nanculef, Claudio Sartori",
"['Hector Allende' 'Emanuele Frandi' 'Ricardo Nanculef' 'Claudio Sartori']"
] |
cs.LG cs.CL cs.NE | null | 1304.1018 | null | null | http://arxiv.org/pdf/1304.1018v2 | 2013-06-12T11:23:34Z | 2013-04-03T17:20:41Z | Estimating Phoneme Class Conditional Probabilities from Raw Speech
Signal using Convolutional Neural Networks | In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic
speech recognition (ASR) system, the phoneme class conditional probabilities
are estimated by first extracting acoustic features from the speech signal
based on prior knowledge such as, speech perception or/and speech production
knowledge, and, then modeling the acoustic features with an ANN. Recent
advances in machine learning techniques, more specifically in the field of
image processing and text processing, have shown that such divide and conquer
strategy (i.e., separating feature extraction and modeling steps) may not be
necessary. Motivated from these studies, in the framework of convolutional
neural networks (CNNs), this paper investigates a novel approach, where the
input to the ANN is raw speech signal and the output is phoneme class
conditional probability estimates. On TIMIT phoneme recognition task, we study
different ANN architectures to show the benefit of CNNs and compare the
proposed approach against conventional approach where, spectral-based feature
MFCC is extracted and modeled by a multilayer perceptron. Our studies show that
the proposed approach can yield comparable or better phoneme recognition
performance when compared to the conventional approach. It indicates that CNNs
can learn features relevant for phoneme classification automatically from the
raw speech signal.
| [
"Dimitri Palaz, Ronan Collobert, Mathew Magimai.-Doss",
"['Dimitri Palaz' 'Ronan Collobert' 'Mathew Magimai. -Doss']"
] |
cs.LG | null | 1304.1192 | null | null | http://arxiv.org/pdf/1304.1192v1 | 2013-04-03T21:14:50Z | 2013-04-03T21:14:50Z | Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch
Stochastic Gradient Descent (SGD) | Distance metric learning (DML) is an important task that has found
applications in many domains. The high computational cost of DML arises from
the large number of variables to be determined and the constraint that a
distance metric has to be a positive semi-definite (PSD) matrix. Although
stochastic gradient descent (SGD) has been successfully applied to improve the
efficiency of DML, it can still be computationally expensive because in order
to ensure that the solution is a PSD matrix, it has to, at every iteration,
project the updated distance metric onto the PSD cone, an expensive operation.
We address this challenge by developing two strategies within SGD, i.e.
mini-batch and adaptive sampling, to effectively reduce the number of updates
(i.e., projections onto the PSD cone) in SGD. We also develop hybrid approaches
that combine the strength of adaptive sampling with that of mini-batch online
learning techniques to further improve the computational efficiency of SGD for
DML. We prove the theoretical guarantees for both adaptive sampling and
mini-batch based approaches for DML. We also conduct an extensive empirical
study to verify the effectiveness of the proposed algorithms for DML.
| [
"Qi Qian, Rong Jin, Jinfeng Yi, Lijun Zhang, Shenghuo Zhu",
"['Qi Qian' 'Rong Jin' 'Jinfeng Yi' 'Lijun Zhang' 'Shenghuo Zhu']"
] |
cs.LG | null | 1304.1391 | null | null | http://arxiv.org/pdf/1304.1391v1 | 2013-04-04T15:08:31Z | 2013-04-04T15:08:31Z | Fast SVM training using approximate extreme points | Applications of non-linear kernel Support Vector Machines (SVMs) to large
datasets is seriously hampered by its excessive training time. We propose a
modification, called the approximate extreme points support vector machine
(AESVM), that is aimed at overcoming this burden. Our approach relies on
conducting the SVM optimization over a carefully selected subset, called the
representative set, of the training dataset. We present analytical results that
indicate the similarity of AESVM and SVM solutions. A linear time algorithm
based on convex hulls and extreme points is used to compute the representative
set in kernel space. Extensive computational experiments on nine datasets
compared AESVM to LIBSVM \citep{LIBSVM}, CVM \citep{Tsang05}, BVM
\citep{Tsang07}, LASVM \citep{Bordes05}, $\text{SVM}^{\text{perf}}$
\citep{Joachims09}, and the random features method \citep{rahimi07}. Our AESVM
implementation was found to train much faster than the other methods, while its
classification accuracy was similar to that of LIBSVM in all cases. In
particular, for a seizure detection dataset, AESVM training was almost $10^3$
times faster than LIBSVM and LASVM and more than forty times faster than CVM
and BVM. Additionally, AESVM also gave competitively fast classification times.
| [
"['Manu Nandan' 'Pramod P. Khargonekar' 'Sachin S. Talathi']",
"Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi"
] |
cs.LG math.PR | null | 1304.1574 | null | null | http://arxiv.org/pdf/1304.1574v1 | 2013-04-04T22:34:55Z | 2013-04-04T22:34:55Z | Generalization Bounds for Domain Adaptation | In this paper, we provide a new framework to obtain the generalization bounds
of the learning process for domain adaptation, and then apply the derived
bounds to analyze the asymptotical convergence of the learning process. Without
loss of generality, we consider two kinds of representative domain adaptation:
one is with multiple sources and the other is combining source and target data.
In particular, we use the integral probability metric to measure the
difference between two domains. For either kind of domain adaptation, we
develop a related Hoeffding-type deviation inequality and a symmetrization
inequality to achieve the corresponding generalization bound based on the
uniform entropy number. We also generalized the classical McDiarmid's
inequality to a more general setting where independent random variables can
take values from different domains. By using this inequality, we then obtain
generalization bounds based on the Rademacher complexity. Afterwards, we
analyze the asymptotic convergence and the rate of convergence of the learning
process for such kind of domain adaptation. Meanwhile, we discuss the factors
that affect the asymptotic behavior of the learning process and the numerical
experiments support our theoretical findings as well.
| [
"Chao Zhang, Lei Zhang, Jieping Ye",
"['Chao Zhang' 'Lei Zhang' 'Jieping Ye']"
] |
cs.SE cs.IR cs.LG | null | 1304.1677 | null | null | http://arxiv.org/pdf/1304.1677v1 | 2013-04-05T11:05:18Z | 2013-04-05T11:05:18Z | Bug Classification: Feature Extraction and Comparison of Event Model
using Na\"ive Bayes Approach | In software industries, individuals at different levels from customer to an
engineer apply diverse mechanisms to detect to which class a particular bug
should be allocated. Sometimes while a simple search in Internet might help, in
many other cases a lot of effort is spent in analyzing the bug report to
classify the bug. So there is a great need of a structured mining algorithm -
where given a crash log, the existing bug database could be mined to find out
the class to which the bug should be allocated. This would involve Mining
patterns and applying different classification algorithms. This paper focuses
on the feature extraction, noise reduction in data and classification of
network bugs using probabilistic Na\"ive Bayes approach. Different event models
like Bernoulli and Multinomial are applied on the extracted features. When new,
unseen bugs are given as input to the algorithms, the performance comparison of
different algorithms is done on the basis of accuracy and recall parameters.
| [
"['Sunil Joy Dommati' 'Ruchi Agrawal' 'Ram Mohana Reddy G.'\n 'S. Sowmya Kamath']",
"Sunil Joy Dommati, Ruchi Agrawal, Ram Mohana Reddy G. and S. Sowmya\n Kamath"
] |
cs.CV cs.DB cs.LG | null | 1304.1995 | null | null | http://arxiv.org/pdf/1304.1995v2 | 2013-04-09T17:59:33Z | 2013-04-07T13:15:17Z | Image Retrieval using Histogram Factorization and Contextual Similarity
Learning | Image retrieval has been a top topic in the field of both computer vision and
machine learning for a long time. Content based image retrieval, which tries to
retrieve images from a database visually similar to a query image, has
attracted much attention. Two most important issues of image retrieval are the
representation and ranking of the images. Recently, bag-of-words based method
has shown its power as a representation method. Moreover, nonnegative matrix
factorization is also a popular way to represent the data samples. In addition,
contextual similarity learning has also been studied and proven to be an
effective method for the ranking problem. However, these technologies have
never been used together. In this paper, we developed an effective image
retrieval system by representing each image using the bag-of-words method as
histograms, and then apply the nonnegative matrix factorization to factorize
the histograms, and finally learn the ranking score using the contextual
similarity learning method. The proposed novel system is evaluated on a large
scale image database and the effectiveness is shown.
| [
"Liu Liang",
"['Liu Liang']"
] |
cs.LG cs.AI cs.MA stat.ML | null | 1304.2024 | null | null | http://arxiv.org/pdf/1304.2024v3 | 2014-03-16T15:10:35Z | 2013-04-07T17:00:37Z | A General Framework for Interacting Bayes-Optimally with Self-Interested
Agents using Arbitrary Parametric Model and Model Prior | Recent advances in Bayesian reinforcement learning (BRL) have shown that
Bayes-optimality is theoretically achievable by modeling the environment's
latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In
self-interested multi-agent environments, the transition dynamics are mainly
controlled by the other agent's stochastic behavior for which FDM's
independence and modeling assumptions do not hold. As a result, FDM does not
allow the other agent's behavior to be generalized across different states nor
specified using prior domain knowledge. To overcome these practical limitations
of FDM, we propose a generalization of BRL to integrate the general class of
parametric models and model priors, thus allowing practitioners' domain
knowledge to be exploited to produce a fine-grained and compact representation
of the other agent's behavior. Empirical evaluation shows that our approach
outperforms existing multi-agent reinforcement learning algorithms.
| [
"Trong Nghia Hoang and Kian Hsiang Low",
"['Trong Nghia Hoang' 'Kian Hsiang Low']"
] |
null | null | 1304.2079 | null | null | http://arxiv.org/pdf/1304.2079v3 | 2014-05-28T00:38:46Z | 2013-04-08T00:06:26Z | Learning Coverage Functions and Private Release of Marginals | We study the problem of approximating and learning coverage functions. A function $c: 2^{[n]} rightarrow mathbf{R}^{+}$ is a coverage function, if there exists a universe $U$ with non-negative weights $w(u)$ for each $u in U$ and subsets $A_1, A_2, ldots, A_n$ of $U$ such that $c(S) = sum_{u in cup_{i in S} A_i} w(u)$. Alternatively, coverage functions can be described as non-negative linear combinations of monotone disjunctions. They are a natural subclass of submodular functions and arise in a number of applications. We give an algorithm that for any $gamma,delta>0$, given random and uniform examples of an unknown coverage function $c$, finds a function $h$ that approximates $c$ within factor $1+gamma$ on all but $delta$-fraction of the points in time $poly(n,1/gamma,1/delta)$. This is the first fully-polynomial algorithm for learning an interesting class of functions in the demanding PMAC model of Balcan and Harvey (2011). Our algorithms are based on several new structural properties of coverage functions. Using the results in (Feldman and Kothari, 2014), we also show that coverage functions are learnable agnostically with excess $ell_1$-error $epsilon$ over all product and symmetric distributions in time $n^{log(1/epsilon)}$. In contrast, we show that, without assumptions on the distribution, learning coverage functions is at least as hard as learning polynomial-size disjoint DNF formulas, a class of functions for which the best known algorithm runs in time $2^{tilde{O}(n^{1/3})}$ (Klivans and Servedio, 2004). As an application of our learning results, we give simple differentially-private algorithms for releasing monotone conjunction counting queries with low average error. In particular, for any $k leq n$, we obtain private release of $k$-way marginals with average error $bar{alpha}$ in time $n^{O(log(1/bar{alpha}))}$. | [
"['Vitaly Feldman' 'Pravesh Kothari']"
] |
stat.ML cs.DC cs.LG | null | 1304.2302 | null | null | http://arxiv.org/pdf/1304.2302v1 | 2013-04-08T18:34:32Z | 2013-04-08T18:34:32Z | ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process
Mixtures | The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian
nonparametric modeling, and is widely used in tasks such as density estimation,
natural language processing, and time series modeling. Although MCMC inference
methods for the DP often provide a gold standard in terms asymptotic accuracy,
they can be computationally expensive and are not obviously parallelizable. We
propose a reparameterization of the Dirichlet process that induces conditional
independencies between the atoms that form the random measure. This conditional
independence enables many of the Markov chain transition operators for DP
inference to be simulated in parallel across multiple cores. Applied to mixture
modeling, our approach enables the Dirichlet process to simultaneously learn
clusters that describe the data and superclusters that define the granularity
of parallelization. Unlike previous approaches, our technique does not require
alteration of the model and leaves the true posterior distribution invariant.
It also naturally lends itself to a distributed software implementation in
terms of Map-Reduce, which we test in cluster configurations of over 50
machines and 100 cores. We present experiments exploring the parallel
efficiency and convergence properties of our approach on both synthetic and
real-world data, including runs on 1MM data vectors in 256 dimensions.
| [
"Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka",
"['Dan Lovell' 'Jonathan Malmaud' 'Ryan P. Adams' 'Vikash K. Mansinghka']"
] |
stat.AP cs.LG stat.ML | null | 1304.2331 | null | null | http://arxiv.org/pdf/1304.2331v1 | 2013-04-08T19:49:51Z | 2013-04-08T19:49:51Z | The PAV algorithm optimizes binary proper scoring rules | There has been much recent interest in application of the
pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the
probabilistic outputs of automatic pattern recognition and machine learning
algorithms. Special cost functions, known as proper scoring rules form natural
objective functions to judge the goodness of such calibration. We show that for
binary pattern classifiers, the non-parametric optimization of calibration,
subject to a monotonicity constraint, can be solved by PAV and that this
solution is optimal for all regular binary proper scoring rules. This extends
previous results which were limited to convex binary proper scoring rules. We
further show that this result holds not only for calibration of probabilities,
but also for calibration of log-likelihood-ratios, in which case optimality
holds independently of the prior probabilities of the pattern classes.
| [
"['Niko Brummer' 'Johan du Preez']",
"Niko Brummer and Johan du Preez"
] |
cs.LG cs.AI stat.ML | null | 1304.2363 | null | null | http://arxiv.org/pdf/1304.2363v1 | 2013-03-27T19:43:53Z | 2013-03-27T19:43:53Z | Multiple decision trees | This paper describes experiments, on two domains, to investigate the effect
of averaging over predictions of multiple decision trees, instead of using a
single tree. Other authors have pointed out theoretical and commonsense reasons
for preferring the multiple tree approach. Ideally, we would like to consider
predictions from all trees, weighted by their probability. However, there is a
vast number of different trees, and it is difficult to estimate the probability
of each tree. We sidestep the estimation problem by using a modified version of
the ID3 algorithm to build good trees, and average over only these trees. Our
results are encouraging. For each domain, we managed to produce a small number
of good trees. We find that it is best to average across sets of trees with
different structure; this usually gives better performance than any of the
constituent trees, including the ID3 tree.
| [
"Suk Wah Kwok, Chris Carter",
"['Suk Wah Kwok' 'Chris Carter']"
] |
cs.CV cs.LG | null | 1304.2490 | null | null | http://arxiv.org/pdf/1304.2490v1 | 2013-04-09T08:45:57Z | 2013-04-09T08:45:57Z | Kernel Reconstruction ICA for Sparse Representation | Independent Component Analysis (ICA) is an effective unsupervised tool to
learn statistically independent representation. However, ICA is not only
sensitive to whitening but also difficult to learn an over-complete basis.
Consequently, ICA with soft Reconstruction cost(RICA) was presented to learn
sparse representations with over-complete basis even on unwhitened data.
Whereas RICA is infeasible to represent the data with nonlinear structure due
to its intrinsic linearity. In addition, RICA is essentially an unsupervised
method and can not utilize the class information. In this paper, we propose a
kernel ICA model with reconstruction constraint (kRICA) to capture the
nonlinear features. To bring in the class information, we further extend the
unsupervised kRICA to a supervised one by introducing a discrimination
constraint, namely d-kRICA. This constraint leads to learn a structured basis
consisted of basis vectors from different basis subsets corresponding to
different class labels. Then each subset will sparsely represent well for its
own class but not for the others. Furthermore, data samples belonging to the
same class will have similar representations, and thereby the learned sparse
representations can take more discriminative power. Experimental results
validate the effectiveness of kRICA and d-kRICA for image classification.
| [
"Yanhui Xiao, Zhenfeng Zhu, Yao Zhao",
"['Yanhui Xiao' 'Zhenfeng Zhu' 'Yao Zhao']"
] |
cond-mat.dis-nn cond-mat.stat-mech cs.LG | 10.1088/1751-8113/46/37/375002 | 1304.2850 | null | null | http://arxiv.org/abs/1304.2850v2 | 2013-08-09T02:15:12Z | 2013-04-10T06:17:07Z | Entropy landscape of solutions in the binary perceptron problem | The statistical picture of the solution space for a binary perceptron is
studied. The binary perceptron learns a random classification of input random
patterns by a set of binary synaptic weights. The learning of this network is
difficult especially when the pattern (constraint) density is close to the
capacity, which is supposed to be intimately related to the structure of the
solution space. The geometrical organization is elucidated by the entropy
landscape from a reference configuration and of solution-pairs separated by a
given Hamming distance in the solution space. We evaluate the entropy at the
annealed level as well as replica symmetric level and the mean field result is
confirmed by the numerical simulations on single instances using the proposed
message passing algorithms. From the first landscape (a random configuration as
a reference), we see clearly how the solution space shrinks as more constraints
are added. From the second landscape of solution-pairs, we deduce the
coexistence of clustering and freezing in the solution space.
| [
"Haiping Huang, K. Y. Michael Wong and Yoshiyuki Kabashima",
"['Haiping Huang' 'K. Y. Michael Wong' 'Yoshiyuki Kabashima']"
] |
stat.AP cs.LG stat.ML | null | 1304.2865 | null | null | http://arxiv.org/pdf/1304.2865v1 | 2013-04-10T07:32:31Z | 2013-04-10T07:32:31Z | The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New
DCF | The change of two orders of magnitude in the 'new DCF' of NIST's SRE'10,
relative to the 'old DCF' evaluation criterion, posed a difficult challenge for
participants and evaluator alike. Initially, participants were at a loss as to
how to calibrate their systems, while the evaluator underestimated the required
number of evaluation trials. After the fact, it is now obvious that both
calibration and evaluation require very large sets of trials. This poses the
challenges of (i) how to decide what number of trials is enough, and (ii) how
to process such large data sets with reasonable memory and CPU requirements.
After SRE'10, at the BOSARIS Workshop, we built solutions to these problems
into the freely available BOSARIS Toolkit. This paper explains the principles
and algorithms behind this toolkit. The main contributions of the toolkit are:
1. The Normalized Bayes Error-Rate Plot, which analyses likelihood- ratio
calibration over a wide range of DCF operating points. These plots also help in
judging the adequacy of the sizes of calibration and evaluation databases. 2.
Efficient algorithms to compute DCF and minDCF for large score files, over the
range of operating points required by these plots. 3. A new score file format,
which facilitates working with very large trial lists. 4. A faster logistic
regression optimizer for fusion and calibration. 5. A principled way to define
EER (equal error rate), which is of practical interest when the absolute error
count is small.
| [
"['Niko Brümmer' 'Edward de Villiers']",
"Niko Br\\\"ummer and Edward de Villiers"
] |
cs.LG | null | 1304.2994 | null | null | http://arxiv.org/pdf/1304.2994v3 | 2014-07-14T01:05:45Z | 2013-04-10T15:26:13Z | A Generalized Online Mirror Descent with Applications to Classification
and Regression | Online learning algorithms are fast, memory-efficient, easy to implement, and
applicable to many prediction problems, including classification, regression,
and ranking. Several online algorithms were proposed in the past few decades,
some based on additive updates, like the Perceptron, and some on multiplicative
updates, like Winnow. A unifying perspective on the design and the analysis of
online algorithms is provided by online mirror descent, a general prediction
strategy from which most first-order algorithms can be obtained as special
cases. We generalize online mirror descent to time-varying regularizers with
generic updates. Unlike standard mirror descent, our more general formulation
also captures second order algorithms, algorithms for composite losses and
algorithms for adaptive filtering. Moreover, we recover, and sometimes improve,
known regret bounds as special cases of our analysis using specific
regularizers. Finally, we show the power of our approach by deriving a new
second order algorithm with a regret bound invariant with respect to arbitrary
rescalings of individual features.
| [
"['Francesco Orabona' 'Koby Crammer' 'Nicolò Cesa-Bianchi']",
"Francesco Orabona, Koby Crammer, Nicol\\`o Cesa-Bianchi"
] |
stat.ML cs.LG | null | 1304.3285 | null | null | http://arxiv.org/pdf/1304.3285v4 | 2013-07-24T19:20:15Z | 2013-04-11T13:20:51Z | Scaling the Indian Buffet Process via Submodular Maximization | Inference for latent feature models is inherently difficult as the inference
space grows exponentially with the size of the input data and number of latent
features. In this work, we use Kurihara & Welling (2008)'s
maximization-expectation framework to perform approximate MAP inference for
linear-Gaussian latent feature models with an Indian Buffet Process (IBP)
prior. This formulation yields a submodular function of the features that
corresponds to a lower bound on the model evidence. By adding a constant to
this function, we obtain a nonnegative submodular function that can be
maximized via a greedy algorithm that obtains at least a one-third
approximation to the optimal solution. Our inference method scales linearly
with the size of the input data, and we show the efficacy of our method on the
largest datasets currently analyzed using an IBP model.
| [
"Colorado Reed and Zoubin Ghahramani",
"['Colorado Reed' 'Zoubin Ghahramani']"
] |
cs.LG math.ST stat.TH | null | 1304.3345 | null | null | http://arxiv.org/pdf/1304.3345v1 | 2013-04-11T15:44:18Z | 2013-04-11T15:44:18Z | Probabilistic Classification using Fuzzy Support Vector Machines | In medical applications such as recognizing the type of a tumor as Malignant
or Benign, a wrong diagnosis can be devastating. Methods like Fuzzy Support
Vector Machines (FSVM) try to reduce the effect of misplaced training points by
assigning a lower weight to the outliers. However, there are still uncertain
points which are similar to both classes and assigning a class by the given
information will cause errors. In this paper, we propose a two-phase
classification method which probabilistically assigns the uncertain points to
each of the classes. The proposed method is applied to the Breast Cancer
Wisconsin (Diagnostic) Dataset which consists of 569 instances in 2 classes of
Malignant and Benign. This method assigns certain instances to their
appropriate classes with probability of one, and the uncertain instances to
each of the classes with associated probabilities. Therefore, based on the
degree of uncertainty, doctors can suggest further examinations before making
the final diagnosis.
| [
"Marzieh Parandehgheibi",
"['Marzieh Parandehgheibi']"
] |
cs.AI cs.CL cs.LG | null | 1304.3432 | null | null | http://arxiv.org/pdf/1304.3432v1 | 2013-03-27T19:56:55Z | 2013-03-27T19:56:55Z | Machine Learning, Clustering, and Polymorphy | This paper describes a machine induction program (WITT) that attempts to
model human categorization. Properties of categories to which human subjects
are sensitive includes best or prototypical members, relative contrasts between
putative categories, and polymorphy (neither necessary or sufficient features).
This approach represents an alternative to usual Artificial Intelligence
approaches to generalization and conceptual clustering which tend to focus on
necessary and sufficient feature rules, equivalence classes, and simple search
and match schemes. WITT is shown to be more consistent with human
categorization while potentially including results produced by more traditional
clustering schemes. Applications of this approach in the domains of expert
systems and information retrieval are also discussed.
| [
"Stephen Jose Hanson, Malcolm Bauer",
"['Stephen Jose Hanson' 'Malcolm Bauer']"
] |
stat.ML cs.LG stat.AP | null | 1304.3568 | null | null | http://arxiv.org/pdf/1304.3568v1 | 2013-04-12T08:47:38Z | 2013-04-12T08:47:38Z | Distributed dictionary learning over a sensor network | We consider the problem of distributed dictionary learning, where a set of
nodes is required to collectively learn a common dictionary from noisy
measurements. This approach may be useful in several contexts including sensor
networks. Diffusion cooperation schemes have been proposed to solve the
distributed linear regression problem. In this work we focus on a
diffusion-based adaptive dictionary learning strategy: each node records
observations and cooperates with its neighbors by sharing its local dictionary.
The resulting algorithm corresponds to a distributed block coordinate descent
(alternate optimization). Beyond dictionary learning, this strategy could be
adapted to many matrix factorization problems and generalized to various
settings. This article presents our approach and illustrates its efficiency on
some numerical examples.
| [
"['Pierre Chainais' 'Cédric Richard']",
"Pierre Chainais and C\\'edric Richard"
] |
cs.LG stat.ML | null | 1304.3708 | null | null | http://arxiv.org/pdf/1304.3708v1 | 2013-04-12T19:09:56Z | 2013-04-12T19:09:56Z | Advice-Efficient Prediction with Expert Advice | Advice-efficient prediction with expert advice (in analogy to label-efficient
prediction) is a variant of prediction with expert advice game, where on each
round of the game we are allowed to ask for advice of a limited number $M$ out
of $N$ experts. This setting is especially interesting when asking for advice
of every expert on every round is expensive. We present an algorithm for
advice-efficient prediction with expert advice that achieves
$O(\sqrt{\frac{N}{M}T\ln N})$ regret on $T$ rounds of the game.
| [
"Yevgeny Seldin and Peter Bartlett and Koby Crammer",
"['Yevgeny Seldin' 'Peter Bartlett' 'Koby Crammer']"
] |
cs.LG stat.ML | 10.5121/ijdkp.2013.3207 | 1304.3745 | null | null | http://arxiv.org/abs/1304.3745v1 | 2013-04-12T22:23:53Z | 2013-04-12T22:23:53Z | Towards more accurate clustering method by using dynamic time warping | An intrinsic problem of classifiers based on machine learning (ML) methods is
that their learning time grows as the size and complexity of the training
dataset increases. For this reason, it is important to have efficient
computational methods and algorithms that can be applied on large datasets,
such that it is still possible to complete the machine learning tasks in
reasonable time. In this context, we present in this paper a more accurate
simple process to speed up ML methods. An unsupervised clustering algorithm is
combined with Expectation, Maximization (EM) algorithm to develop an efficient
Hidden Markov Model (HMM) training. The idea of the proposed process consists
of two steps. In the first step, training instances with similar inputs are
clustered and a weight factor which represents the frequency of these instances
is assigned to each representative cluster. Dynamic Time Warping technique is
used as a dissimilarity function to cluster similar examples. In the second
step, all formulas in the classical HMM training algorithm (EM) associated with
the number of training instances are modified to include the weight factor in
appropriate terms. This process significantly accelerates HMM training while
maintaining the same initial, transition and emission probabilities matrixes as
those obtained with the classical HMM training algorithm. Accordingly, the
classification accuracy is preserved. Depending on the size of the training
set, speedups of up to 2200 times is possible when the size is about 100.000
instances. The proposed approach is not limited to training HMMs, but it can be
employed for a large variety of MLs methods.
| [
"['Khadoudja Ghanem']",
"Khadoudja Ghanem"
] |
stat.ME cs.LG q-bio.QM stat.AP stat.ML | null | 1304.3760 | null | null | http://arxiv.org/pdf/1304.3760v3 | 2016-09-21T23:59:53Z | 2013-04-13T02:15:20Z | Identification of relevant subtypes via preweighted sparse clustering | Cluster analysis methods are used to identify homogeneous subgroups in a data
set. In biomedical applications, one frequently applies cluster analysis in
order to identify biologically interesting subgroups. In particular, one may
wish to identify subgroups that are associated with a particular outcome of
interest. Conventional clustering methods generally do not identify such
subgroups, particularly when there are a large number of high-variance features
in the data set. Conventional methods may identify clusters associated with
these high-variance features when one wishes to obtain secondary clusters that
are more interesting biologically or more strongly associated with a particular
outcome of interest. A modification of sparse clustering can be used to
identify such secondary clusters or clusters associated with an outcome of
interest. This method correctly identifies such clusters of interest in several
simulation scenarios. The method is also applied to a large prospective cohort
study of temporomandibular disorders and a leukemia microarray data set.
| [
"Sheila Gaynor and Eric Bair",
"['Sheila Gaynor' 'Eric Bair']"
] |
cs.LG | 10.5120/11267-6526 | 1304.3840 | null | null | http://arxiv.org/abs/1304.3840v1 | 2013-04-13T20:19:25Z | 2013-04-13T20:19:25Z | A New Homogeneity Inter-Clusters Measure in SemiSupervised Clustering | Many studies in data mining have proposed a new learning called
semi-Supervised. Such type of learning combines unlabeled and labeled data
which are hard to obtain. However, in unsupervised methods, the only unlabeled
data are used. The problem of significance and the effectiveness of
semi-supervised clustering results is becoming of main importance. This paper
pursues the thesis that muchgreater accuracy can be achieved in such clustering
by improving the similarity computing. Hence, we introduce a new approach of
semisupervised clustering using an innovative new homogeneity measure of
generated clusters. Our experimental results demonstrate significantly improved
accuracy as a result.
| [
"Badreddine Meftahi, Ourida Ben Boubaker Saidi",
"['Badreddine Meftahi' 'Ourida Ben Boubaker Saidi']"
] |
stat.ME cs.CV cs.LG | null | 1304.4077 | null | null | http://arxiv.org/pdf/1304.4077v2 | 2013-05-31T16:57:33Z | 2013-04-15T12:54:52Z | A new Bayesian ensemble of trees classifier for identifying multi-class
labels in satellite images | Classification of satellite images is a key component of many remote sensing
applications. One of the most important products of a raw satellite image is
the classified map which labels the image pixels into meaningful classes.
Though several parametric and non-parametric classifiers have been developed
thus far, accurate labeling of the pixels still remains a challenge. In this
paper, we propose a new reliable multiclass-classifier for identifying class
labels of a satellite image in remote sensing applications. The proposed
multiclass-classifier is a generalization of a binary classifier based on the
flexible ensemble of regression trees model called Bayesian Additive Regression
Trees (BART). We used three small areas from the LANDSAT 5 TM image, acquired
on August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) over
Kings County, Nova Scotia, Canada to classify the land-use. Several prediction
accuracy and uncertainty measures have been used to compare the reliability of
the proposed classifier with the state-of-the-art classifiers in remote
sensing.
| [
"Reshu Agarwal, Pritam Ranjan, Hugh Chipman",
"['Reshu Agarwal' 'Pritam Ranjan' 'Hugh Chipman']"
] |
cs.LG cs.CV stat.ML | 10.1007/978-3-642-33709-3_16 | 1304.4344 | null | null | http://arxiv.org/abs/1304.4344v1 | 2013-04-16T06:47:03Z | 2013-04-16T06:47:03Z | Sparse Coding and Dictionary Learning for Symmetric Positive Definite
Matrices: A Kernel Approach | Recent advances suggest that a wide range of computer vision problems can be
addressed more appropriately by considering non-Euclidean geometry. This paper
tackles the problem of sparse coding and dictionary learning in the space of
symmetric positive definite matrices, which form a Riemannian manifold. With
the aid of the recently introduced Stein kernel (related to a symmetric version
of Bregman matrix divergence), we propose to perform sparse coding by embedding
Riemannian manifolds into reproducing kernel Hilbert spaces. This leads to a
convex and kernel version of the Lasso problem, which can be solved
efficiently. We furthermore propose an algorithm for learning a Riemannian
dictionary (used for sparse coding), closely tied to the Stein kernel.
Experiments on several classification tasks (face recognition, texture
classification, person re-identification) show that the proposed sparse coding
approach achieves notable improvements in discrimination accuracy, in
comparison to state-of-the-art methods such as tensor sparse coding, Riemannian
locality preserving projection, and symmetry-driven accumulation of local
features.
| [
"Mehrtash T. Harandi, Conrad Sanderson, Richard Hartley, Brian C.\n Lovell",
"['Mehrtash T. Harandi' 'Conrad Sanderson' 'Richard Hartley'\n 'Brian C. Lovell']"
] |
cs.IT cs.LG math.IT math.NA stat.ML | null | 1304.4610 | null | null | http://arxiv.org/pdf/1304.4610v2 | 2013-05-01T00:29:31Z | 2013-04-16T20:26:15Z | Spectral Compressed Sensing via Structured Matrix Completion | The paper studies the problem of recovering a spectrally sparse object from a
small number of time domain samples. Specifically, the object of interest with
ambient dimension $n$ is assumed to be a mixture of $r$ complex
multi-dimensional sinusoids, while the underlying frequencies can assume any
value in the unit disk. Conventional compressed sensing paradigms suffer from
the {\em basis mismatch} issue when imposing a discrete dictionary on the
Fourier representation. To address this problem, we develop a novel
nonparametric algorithm, called enhanced matrix completion (EMaC), based on
structured matrix completion. The algorithm starts by arranging the data into a
low-rank enhanced form with multi-fold Hankel structure, then attempts recovery
via nuclear norm minimization. Under mild incoherence conditions, EMaC allows
perfect recovery as soon as the number of samples exceeds the order of
$\mathcal{O}(r\log^{2} n)$. We also show that, in many instances, accurate
completion of a low-rank multi-fold Hankel matrix is possible when the number
of observed entries is proportional to the information theoretical limits
(except for a logarithmic gap). The robustness of EMaC against bounded noise
and its applicability to super resolution are further demonstrated by numerical
experiments.
| [
"Yuxin Chen, Yuejie Chi",
"['Yuxin Chen' 'Yuejie Chi']"
] |
cs.DS cs.LG cs.LO | null | 1304.4633 | null | null | http://arxiv.org/pdf/1304.4633v1 | 2013-04-16T22:10:26Z | 2013-04-16T22:10:26Z | PAC Quasi-automatizability of Resolution over Restricted Distributions | We consider principled alternatives to unsupervised learning in data mining
by situating the learning task in the context of the subsequent analysis task.
Specifically, we consider a query-answering (hypothesis-testing) task: In the
combined task, we decide whether an input query formula is satisfied over a
background distribution by using input examples directly, rather than invoking
a two-stage process in which (i) rules over the distribution are learned by an
unsupervised learning algorithm and (ii) a reasoning algorithm decides whether
or not the query formula follows from the learned rules. In a previous work
(2013), we observed that the learning task could satisfy numerous desirable
criteria in this combined context -- effectively matching what could be
achieved by agnostic learning of CNFs from partial information -- that are not
known to be achievable directly. In this work, we show that likewise, there are
reasoning tasks that are achievable in such a combined context that are not
known to be achievable directly (and indeed, have been seriously conjectured to
be impossible, cf. (Alekhnovich and Razborov, 2008)). Namely, we test for a
resolution proof of the query formula of a given size in quasipolynomial time
(that is, "quasi-automatizing" resolution). The learning setting we consider is
a partial-information, restricted-distribution setting that generalizes
learning parities over the uniform distribution from partial information,
another task that is known not to be achievable directly in various models (cf.
(Ben-David and Dichterman, 1998) and (Michael, 2010)).
| [
"Brendan Juba",
"['Brendan Juba']"
] |
quant-ph cs.CC cs.LG | 10.4230/LIPIcs.TQC.2013.50 | 1304.4642 | null | null | http://arxiv.org/abs/1304.4642v1 | 2013-04-16T23:24:38Z | 2013-04-16T23:24:38Z | Easy and hard functions for the Boolean hidden shift problem | We study the quantum query complexity of the Boolean hidden shift problem.
Given oracle access to f(x+s) for a known Boolean function f, the task is to
determine the n-bit string s. The quantum query complexity of this problem
depends strongly on f. We demonstrate that the easiest instances of this
problem correspond to bent functions, in the sense that an exact one-query
algorithm exists if and only if the function is bent. We partially characterize
the hardest instances, which include delta functions. Moreover, we show that
the problem is easy for random functions, since two queries suffice. Our
algorithm for random functions is based on performing the pretty good
measurement on several copies of a certain state; its analysis relies on the
Fourier transform. We also use this approach to improve the quantum rejection
sampling approach to the Boolean hidden shift problem.
| [
"['Andrew M. Childs' 'Robin Kothari' 'Maris Ozols' 'Martin Roetteler']",
"Andrew M. Childs, Robin Kothari, Maris Ozols, Martin Roetteler"
] |
cs.LG q-bio.QM stat.ML | 10.1109/TIT.2019.2961814 | 1304.4806 | null | null | http://arxiv.org/abs/1304.4806v4 | 2019-12-25T18:08:49Z | 2013-04-17T13:06:59Z | Unsupervised model-free representation learning | Numerous control and learning problems face the situation where sequences of
high-dimensional highly dependent data are available but no or little feedback
is provided to the learner, which makes any inference rather challenging. To
address this challenge, we formulate the following problem. Given a series of
observations $X_0,\dots,X_n$ coming from a large (high-dimensional) space
$\mathcal X$, find a representation function $f$ mapping $\mathcal X$ to a
finite space $\mathcal Y$ such that the series $f(X_0),\dots,f(X_n)$ preserves
as much information as possible about the original time-series dependence in
$X_0,\dots,X_n$. We show that, for stationary time series, the function $f$ can
be selected as the one maximizing a certain information criterion that we call
time-series information. Some properties of this functions are investigated,
including its uniqueness and consistency of its empirical estimates.
Implications for the problem of optimal control are presented.
| [
"Daniil Ryabko",
"['Daniil Ryabko']"
] |
cs.CV cs.LG cs.MM | null | 1304.5063 | null | null | http://arxiv.org/pdf/1304.5063v2 | 2013-04-26T11:49:10Z | 2013-04-18T09:40:12Z | Combinaison d'information visuelle, conceptuelle, et contextuelle pour
la construction automatique de hierarchies semantiques adaptees a
l'annotation d'images | This paper proposes a new methodology to automatically build semantic
hierarchies suitable for image annotation and classification. The building of
the hierarchy is based on a new measure of semantic similarity. The proposed
measure incorporates several sources of information: visual, conceptual and
contextual as we defined in this paper. The aim is to provide a measure that
best represents image semantics. We then propose rules based on this measure,
for the building of the final hierarchy, and which explicitly encode
hierarchical relationships between different concepts. Therefore, the built
hierarchy is used in a semantic hierarchical classification framework for image
annotation. Our experiments and results show that the hierarchy built improves
classification results.
Ce papier propose une nouvelle methode pour la construction automatique de
hierarchies semantiques adaptees a la classification et a l'annotation
d'images. La construction de la hierarchie est basee sur une nouvelle mesure de
similarite semantique qui integre plusieurs sources d'informations: visuelle,
conceptuelle et contextuelle que nous definissons dans ce papier. L'objectif
est de fournir une mesure qui est plus proche de la semantique des images. Nous
proposons ensuite des regles, basees sur cette mesure, pour la construction de
la hierarchie finale qui encode explicitement les relations hierarchiques entre
les differents concepts. La hierarchie construite est ensuite utilisee dans un
cadre de classification semantique hierarchique d'images en concepts visuels.
Nos experiences et resultats montrent que la hierarchie construite permet
d'ameliorer les resultats de la classification.
| [
"Hichem Bannour and C\\'eline Hudelot",
"['Hichem Bannour' 'Céline Hudelot']"
] |
cs.IR cs.LG | null | 1304.5168 | null | null | http://arxiv.org/pdf/1304.5168v1 | 2013-04-18T15:57:34Z | 2013-04-18T15:57:34Z | Image Retrieval based on Bag-of-Words model | This article gives a survey for bag-of-words (BoW) or bag-of-features model
in image retrieval system. In recent years, large-scale image retrieval shows
significant potential in both industry applications and research problems. As
local descriptors like SIFT demonstrate great discriminative power in solving
vision problems like object recognition, image classification and annotation,
more and more state-of-the-art large scale image retrieval systems are trying
to rely on them. A common way to achieve this is first quantizing local
descriptors into visual words, and then applying scalable textual indexing and
retrieval schemes. We call this model as bag-of-words or bag-of-features model.
The goal of this survey is to give an overview of this model and introduce
different strategies when building the system based on this model.
| [
"['Jialu Liu']",
"Jialu Liu"
] |
cs.LG stat.ML | null | 1304.5299 | null | null | http://arxiv.org/pdf/1304.5299v4 | 2014-02-14T07:42:15Z | 2013-04-19T02:51:52Z | Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget | Can we make Bayesian posterior MCMC sampling more efficient when faced with
very large datasets? We argue that computing the likelihood for N datapoints in
the Metropolis-Hastings (MH) test to reach a single binary decision is
computationally inefficient. We introduce an approximate MH rule based on a
sequential hypothesis test that allows us to accept or reject samples with high
confidence using only a fraction of the data required for the exact MH rule.
While this method introduces an asymptotic bias, we show that this bias can be
controlled and is more than offset by a decrease in variance due to our ability
to draw more samples per unit of time.
| [
"Anoop Korattikara, Yutian Chen, Max Welling",
"['Anoop Korattikara' 'Yutian Chen' 'Max Welling']"
] |
cs.LG stat.ML | 10.1007/978-3-642-40988-2_15 | 1304.5350 | null | null | http://arxiv.org/abs/1304.5350v3 | 2013-09-02T07:57:23Z | 2013-04-19T09:11:34Z | Parallel Gaussian Process Optimization with Upper Confidence Bound and
Pure Exploration | In this paper, we consider the challenge of maximizing an unknown function f
for which evaluations are noisy and are acquired with high cost. An iterative
procedure uses the previous measures to actively select the next estimation of
f which is predicted to be the most useful. We focus on the case where the
function can be evaluated in parallel with batches of fixed size and analyze
the benefit compared to the purely sequential procedure in terms of cumulative
regret. We introduce the Gaussian Process Upper Confidence Bound and Pure
Exploration algorithm (GP-UCB-PE) which combines the UCB strategy and Pure
Exploration in the same batch of evaluations along the parallel iterations. We
prove theoretical upper bounds on the regret with batches of size K for this
procedure which show the improvement of the order of sqrt{K} for fixed
iteration cost over purely sequential versions. Moreover, the multiplicative
constants involved have the property of being dimension-free. We also confirm
empirically the efficiency of GP-UCB-PE on real and synthetic problems compared
to state-of-the-art competitors.
| [
"['Emile Contal' 'David Buffoni' 'Alexandre Robicquet' 'Nicolas Vayatis']",
"Emile Contal and David Buffoni and Alexandre Robicquet and Nicolas\n Vayatis"
] |
cs.IR cs.DL cs.LG | null | 1304.5457 | null | null | http://arxiv.org/pdf/1304.5457v1 | 2013-04-19T15:53:53Z | 2013-04-19T15:53:53Z | Personalized Academic Research Paper Recommendation System | A huge number of academic papers are coming out from a lot of conferences and
journals these days. In these circumstances, most researchers rely on key-based
search or browsing through proceedings of top conferences and journals to find
their related work. To ease this difficulty, we propose a Personalized Academic
Research Paper Recommendation System, which recommends related articles, for
each researcher, that may be interesting to her/him. In this paper, we first
introduce our web crawler to retrieve research papers from the web. Then, we
define similarity between two research papers based on the text similarity
between them. Finally, we propose our recommender system developed using
collaborative filtering methods. Our evaluation results demonstrate that our
system recommends good quality research papers.
| [
"Joonseok Lee, Kisung Lee, Jennifer G. Kim",
"['Joonseok Lee' 'Kisung Lee' 'Jennifer G. Kim']"
] |
cs.LG stat.ML | null | 1304.5504 | null | null | http://arxiv.org/pdf/1304.5504v6 | 2016-05-24T05:07:06Z | 2013-04-19T18:51:07Z | Optimal Stochastic Strongly Convex Optimization with a Logarithmic
Number of Projections | We consider stochastic strongly convex optimization with a complex inequality
constraint. This complex inequality constraint may lead to computationally
expensive projections in algorithmic iterations of the stochastic gradient
descent~(SGD) methods. To reduce the computation costs pertaining to the
projections, we propose an Epoch-Projection Stochastic Gradient
Descent~(Epro-SGD) method. The proposed Epro-SGD method consists of a sequence
of epochs; it applies SGD to an augmented objective function at each iteration
within the epoch, and then performs a projection at the end of each epoch.
Given a strongly convex optimization and for a total number of $T$ iterations,
Epro-SGD requires only $\log(T)$ projections, and meanwhile attains an optimal
convergence rate of $O(1/T)$, both in expectation and with a high probability.
To exploit the structure of the optimization problem, we propose a proximal
variant of Epro-SGD, namely Epro-ORDA, based on the optimal regularized dual
averaging method. We apply the proposed methods on real-world applications; the
empirical results demonstrate the effectiveness of our methods.
| [
"['Jianhui Chen' 'Tianbao Yang' 'Qihang Lin' 'Lijun Zhang' 'Yi Chang']",
"Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang"
] |
cs.LG stat.ML | null | 1304.5575 | null | null | http://arxiv.org/pdf/1304.5575v2 | 2013-04-25T11:46:51Z | 2013-04-20T00:57:35Z | Inverse Density as an Inverse Problem: The Fredholm Equation Approach | In this paper we address the problem of estimating the ratio $\frac{q}{p}$
where $p$ is a density function and $q$ is another density, or, more generally
an arbitrary function. Knowing or approximating this ratio is needed in various
problems of inference and integration, in particular, when one needs to average
a function with respect to one probability distribution, given a sample from
another. It is often referred as {\it importance sampling} in statistical
inference and is also closely related to the problem of {\it covariate shift}
in transfer learning as well as to various MCMC methods. It may also be useful
for separating the underlying geometry of a space, say a manifold, from the
density function defined on it.
Our approach is based on reformulating the problem of estimating
$\frac{q}{p}$ as an inverse problem in terms of an integral operator
corresponding to a kernel, and thus reducing it to an integral equation, known
as the Fredholm problem of the first kind. This formulation, combined with the
techniques of regularization and kernel methods, leads to a principled
kernel-based framework for constructing algorithms and for analyzing them
theoretically.
The resulting family of algorithms (FIRE, for Fredholm Inverse Regularized
Estimator) is flexible, simple and easy to implement.
We provide detailed theoretical analysis including concentration bounds and
convergence rates for the Gaussian kernel in the case of densities defined on
$\R^d$, compact domains in $\R^d$ and smooth $d$-dimensional sub-manifolds of
the Euclidean space.
We also show experimental results including applications to classification
and semi-supervised learning within the covariate shift framework and
demonstrate some encouraging experimental comparisons. We also show how the
parameters of our algorithms can be chosen in a completely unsupervised manner.
| [
"['Qichao Que' 'Mikhail Belkin']",
"Qichao Que and Mikhail Belkin"
] |
cs.CV cs.DC cs.LG stat.ML | null | 1304.5583 | null | null | http://arxiv.org/pdf/1304.5583v2 | 2013-10-16T02:55:18Z | 2013-04-20T03:54:48Z | Distributed Low-rank Subspace Segmentation | Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups.
| [
"Ameet Talwalkar, Lester Mackey, Yadong Mu, Shih-Fu Chang, Michael I.\n Jordan",
"['Ameet Talwalkar' 'Lester Mackey' 'Yadong Mu' 'Shih-Fu Chang'\n 'Michael I. Jordan']"
] |
cs.LG | null | 1304.5634 | null | null | http://arxiv.org/pdf/1304.5634v1 | 2013-04-20T14:43:35Z | 2013-04-20T14:43:35Z | A Survey on Multi-view Learning | In recent years, a great many methods of learning from multi-view data by
considering the diversity of different views have been proposed. These views
may be obtained from multiple sources or different feature subsets. In trying
to organize and highlight similarities and differences between the variety of
multi-view learning approaches, we review a number of representative multi-view
learning algorithms in different areas and classify them into three groups: 1)
co-training, 2) multiple kernel learning, and 3) subspace learning. Notably,
co-training style algorithms train alternately to maximize the mutual agreement
on two distinct views of the data; multiple kernel learning algorithms exploit
kernels that naturally correspond to different views and combine kernels either
linearly or non-linearly to improve learning performance; and subspace learning
algorithms aim to obtain a latent subspace shared by multiple views by assuming
that the input views are generated from this latent subspace. Though there is
significant variance in the approaches to integrating multiple views to improve
learning performance, they mainly exploit either the consensus principle or the
complementary principle to ensure the success of multi-view learning. Since
accessing multiple views is the fundament of multi-view learning, with the
exception of study on learning a model from multiple views, it is also valuable
to study how to construct multiple views and how to evaluate these views.
Overall, by exploring the consistency and complementary properties of different
views, multi-view learning is rendered more effective, more promising, and has
better generalization ability than single-view learning.
| [
"Chang Xu, Dacheng Tao, Chao Xu",
"['Chang Xu' 'Dacheng Tao' 'Chao Xu']"
] |
cs.LG stat.ML | null | 1304.5678 | null | null | http://arxiv.org/pdf/1304.5678v1 | 2013-04-20T23:38:45Z | 2013-04-20T23:38:45Z | Analytic Feature Selection for Support Vector Machines | Support vector machines (SVMs) rely on the inherent geometry of a data set to
classify training data. Because of this, we believe SVMs are an excellent
candidate to guide the development of an analytic feature selection algorithm,
as opposed to the more commonly used heuristic methods. We propose a
filter-based feature selection algorithm based on the inherent geometry of a
feature set. Through observation, we identified six geometric properties that
differ between optimal and suboptimal feature sets, and have statistically
significant correlations to classifier performance. Our algorithm is based on
logistic and linear regression models using these six geometric properties as
predictor variables. The proposed algorithm achieves excellent results on high
dimensional text data sets, with features that can be organized into a handful
of feature types; for example, unigrams, bigrams or semantic structural
features. We believe this algorithm is a novel and effective approach to
solving the feature selection problem for linear SVMs.
| [
"Carly Stambaugh, Hui Yang, Felix Breuer",
"['Carly Stambaugh' 'Hui Yang' 'Felix Breuer']"
] |
stat.ML cs.LG | null | 1304.5758 | null | null | http://arxiv.org/pdf/1304.5758v2 | 2013-10-03T00:48:53Z | 2013-04-21T15:58:56Z | Prior-free and prior-dependent regret bounds for Thompson Sampling | We consider the stochastic multi-armed bandit problem with a prior
distribution on the reward distributions. We are interested in studying
prior-free and prior-dependent regret bounds, very much in the same spirit as
the usual distribution-free and distribution-dependent bounds for the
non-Bayesian stochastic bandit. Building on the techniques of Audibert and
Bubeck [2009] and Russo and Roy [2013] we first show that Thompson Sampling
attains an optimal prior-free bound in the sense that for any prior
distribution its Bayesian regret is bounded from above by $14 \sqrt{n K}$. This
result is unimprovable in the sense that there exists a prior distribution such
that any algorithm has a Bayesian regret bounded from below by $\frac{1}{20}
\sqrt{n K}$. We also study the case of priors for the setting of Bubeck et al.
[2013] (where the optimal mean is known as well as a lower bound on the
smallest gap) and we show that in this case the regret of Thompson Sampling is
in fact uniformly bounded over time, thus showing that Thompson Sampling can
greatly take advantage of the nice properties of these priors.
| [
"['Sébastien Bubeck' 'Che-Yu Liu']",
"S\\'ebastien Bubeck and Che-Yu Liu"
] |
cs.LG | null | 1304.5793 | null | null | http://arxiv.org/pdf/1304.5793v4 | 2014-08-22T14:59:13Z | 2013-04-21T20:03:23Z | Continuum armed bandit problem of few variables in high dimensions | We consider the stochastic and adversarial settings of continuum armed
bandits where the arms are indexed by [0,1]^d. The reward functions r:[0,1]^d
-> R are assumed to intrinsically depend on at most k coordinate variables
implying r(x_1,..,x_d) = g(x_{i_1},..,x_{i_k}) for distinct and unknown
i_1,..,i_k from {1,..,d} and some locally Holder continuous g:[0,1]^k -> R with
exponent 0 < alpha <= 1. Firstly, assuming (i_1,..,i_k) to be fixed across
time, we propose a simple modification of the CAB1 algorithm where we construct
the discrete set of sampling points to obtain a bound of
O(n^((alpha+k)/(2*alpha+k)) (log n)^((alpha)/(2*alpha+k)) C(k,d)) on the
regret, with C(k,d) depending at most polynomially in k and sub-logarithmically
in d. The construction is based on creating partitions of {1,..,d} into k
disjoint subsets and is probabilistic, hence our result holds with high
probability. Secondly we extend our results to also handle the more general
case where (i_1,...,i_k) can change over time and derive regret bounds for the
same.
| [
"['Hemant Tyagi' 'Bernd Gärtner']",
"Hemant Tyagi and Bernd G\\\"artner"
] |
cs.LG cs.SD stat.ML | null | 1304.5862 | null | null | http://arxiv.org/pdf/1304.5862v2 | 2013-05-29T17:36:07Z | 2013-04-22T07:44:05Z | Multi-Label Classifier Chains for Bird Sound | Bird sound data collected with unattended microphones for automatic surveys,
or mobile devices for citizen science, typically contain multiple
simultaneously vocalizing birds of different species. However, few works have
considered the multi-label structure in birdsong. We propose to use an ensemble
of classifier chains combined with a histogram-of-segments representation for
multi-label classification of birdsong. The proposed method is compared with
binary relevance and three multi-instance multi-label learning (MIML)
algorithms from prior work (which focus more on structure in the sound, and
less on structure in the label sets). Experiments are conducted on two
real-world birdsong datasets, and show that the proposed method usually
outperforms binary relevance (using the same features and base-classifier), and
is better in some cases and worse in others compared to the MIML algorithms.
| [
"['Forrest Briggs' 'Xiaoli Z. Fern' 'Jed Irvine']",
"Forrest Briggs, Xiaoli Z. Fern, Jed Irvine"
] |
cs.CV cs.LG | null | 1304.5894 | null | null | http://arxiv.org/pdf/1304.5894v2 | 2013-04-23T09:00:01Z | 2013-04-22T09:46:47Z | Bayesian crack detection in ultra high resolution multimodal images of
paintings | The preservation of our cultural heritage is of paramount importance. Thanks
to recent developments in digital acquisition techniques, powerful image
analysis algorithms are developed which can be useful non-invasive tools to
assist in the restoration and preservation of art. In this paper we propose a
semi-supervised crack detection method that can be used for high-dimensional
acquisitions of paintings coming from different modalities. Our dataset
consists of a recently acquired collection of images of the Ghent Altarpiece
(1432), one of Northern Europe's most important art masterpieces. Our goal is
to build a classifier that is able to discern crack pixels from the background
consisting of non-crack pixels, making optimal use of the information that is
provided by each modality. To accomplish this we employ a recently developed
non-parametric Bayesian classifier, that uses tensor factorizations to
characterize any conditional probability. A prior is placed on the parameters
of the factorization such that every possible interaction between predictors is
allowed while still identifying a sparse subset among these predictors. The
proposed Bayesian classifier, which we will refer to as conditional Bayesian
tensor factorization or CBTF, is assessed by visually comparing classification
results with the Random Forest (RF) algorithm.
| [
"Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid\n Daubechies, David Dunson",
"['Bruno Cornelis' 'Yun Yang' 'Joshua T. Vogelstein' 'Ann Dooms'\n 'Ingrid Daubechies' 'David Dunson']"
] |
cs.SI cs.LG physics.soc-ph stat.ME | 10.1007/978-3-642-37210-0_22 | 1304.5974 | null | null | http://arxiv.org/abs/1304.5974v1 | 2013-04-22T15:07:19Z | 2013-04-22T15:07:19Z | Dynamic stochastic blockmodels: Statistical models for time-evolving
networks | Significant efforts have gone into the development of statistical models for
analyzing data in the form of networks, such as social networks. Most existing
work has focused on modeling static networks, which represent either a single
time snapshot or an aggregate view over time. There has been recent interest in
statistical modeling of dynamic networks, which are observed at multiple points
in time and offer a richer representation of many complex phenomena. In this
paper, we propose a state-space model for dynamic networks that extends the
well-known stochastic blockmodel for static networks to the dynamic setting. We
then propose a procedure to fit the model using a modification of the extended
Kalman filter augmented with a local search. We apply the procedure to analyze
a dynamic social network of email communication.
| [
"['Kevin S. Xu' 'Alfred O. Hero III']",
"Kevin S. Xu and Alfred O. Hero III"
] |
cs.LG cs.AI | null | 1304.6383 | null | null | http://arxiv.org/pdf/1304.6383v2 | 2014-01-25T10:46:53Z | 2013-04-23T19:24:02Z | The Stochastic Gradient Descent for the Primal L1-SVM Optimization
Revisited | We reconsider the stochastic (sub)gradient approach to the unconstrained
primal L1-SVM optimization. We observe that if the learning rate is inversely
proportional to the number of steps, i.e., the number of times any training
pattern is presented to the algorithm, the update rule may be transformed into
the one of the classical perceptron with margin in which the margin threshold
increases linearly with the number of steps. Moreover, if we cycle repeatedly
through the possibly randomly permuted training set the dual variables defined
naturally via the expansion of the weight vector as a linear combination of the
patterns on which margin errors were made are shown to obey at the end of each
complete cycle automatically the box constraints arising in dual optimization.
This renders the dual Lagrangian a running lower bound on the primal objective
tending to it at the optimum and makes available an upper bound on the relative
accuracy achieved which provides a meaningful stopping criterion. In addition,
we propose a mechanism of presenting the same pattern repeatedly to the
algorithm which maintains the above properties. Finally, we give experimental
evidence that algorithms constructed along these lines exhibit a considerably
improved performance.
| [
"['Constantinos Panagiotakopoulos' 'Petroula Tsampouka']",
"Constantinos Panagiotakopoulos and Petroula Tsampouka"
] |
cs.LG stat.ME stat.ML | null | 1304.6478 | null | null | http://arxiv.org/pdf/1304.6478v1 | 2013-04-24T03:59:39Z | 2013-04-24T03:59:39Z | The K-modes algorithm for clustering | Many clustering algorithms exist that estimate a cluster centroid, such as
K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters
data by returning exactly K meaningful modes. We propose a natural definition
of a K-modes objective function by combining the notions of density and cluster
assignment. The algorithm becomes K-means and K-medoids in the limit of very
large and very small scales. Computationally, it is slightly slower than
K-means but much faster than mean-shift or K-medoids. Unlike K-means, it is
able to find centroids that are valid patterns, truly representative of a
cluster, even with nonconvex clusters, and appears robust to outliers and
misspecification of the scale and number of clusters.
| [
"Miguel \\'A. Carreira-Perpi\\~n\\'an and Weiran Wang",
"['Miguel Á. Carreira-Perpiñán' 'Weiran Wang']"
] |
cs.LG cs.IR stat.ML | null | 1304.6480 | null | null | http://arxiv.org/pdf/1304.6480v1 | 2013-04-24T04:08:23Z | 2013-04-24T04:08:23Z | A Theoretical Analysis of NDCG Type Ranking Measures | A central problem in ranking is to design a ranking measure for evaluation of
ranking functions. In this paper we study, from a theoretical perspective, the
widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures.
Although there are extensive empirical studies of NDCG, little is known about
its theoretical properties. We first show that, whatever the ranking function
is, the standard NDCG which adopts a logarithmic discount, converges to 1 as
the number of items to rank goes to infinity. On the first sight, this result
is very surprising. It seems to imply that NDCG cannot differentiate good and
bad ranking functions, contradicting to the empirical success of NDCG in many
applications. In order to have a deeper understanding of ranking measures in
general, we propose a notion referred to as consistent distinguishability. This
notion captures the intuition that a ranking measure should have such a
property: For every pair of substantially different ranking functions, the
ranking measure can decide which one is better in a consistent manner on almost
all datasets. We show that NDCG with logarithmic discount has consistent
distinguishability although it converges to the same limit for all ranking
functions. We next characterize the set of all feasible discount functions for
NDCG according to the concept of consistent distinguishability. Specifically we
show that whether NDCG has consistent distinguishability depends on how fast
the discount decays, and 1/r is a critical point. We then turn to the cut-off
version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for
various choices of k and the discount functions. Experimental results on real
Web search datasets agree well with the theory.
| [
"['Yining Wang' 'Liwei Wang' 'Yuanzhi Li' 'Di He' 'Tie-Yan Liu' 'Wei Chen']",
"Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, Wei Chen"
] |
cs.LG stat.ML | null | 1304.6487 | null | null | http://arxiv.org/pdf/1304.6487v2 | 2017-05-16T13:28:28Z | 2013-04-24T06:37:07Z | Locally linear representation for image clustering | It is a key to construct a similarity graph in graph-oriented subspace
learning and clustering. In a similarity graph, each vertex denotes a data
point and the edge weight represents the similarity between two points. There
are two popular schemes to construct a similarity graph, i.e., pairwise
distance based scheme and linear representation based scheme. Most existing
works have only involved one of the above schemes and suffered from some
limitations. Specifically, pairwise distance based methods are sensitive to the
noises and outliers compared with linear representation based methods. On the
other hand, there is the possibility that linear representation based
algorithms wrongly select inter-subspaces points to represent a point, which
will degrade the performance. In this paper, we propose an algorithm, called
Locally Linear Representation (LLR), which integrates pairwise distance with
linear representation together to address the problems. The proposed algorithm
can automatically encode each data point over a set of points that not only
could denote the objective point with less residual error, but also are close
to the point in Euclidean space. The experimental results show that our
approach is promising in subspace learning and subspace clustering.
| [
"['Liangli Zhen' 'Zhang Yi' 'Xi Peng' 'Dezhong Peng']",
"Liangli Zhen, Zhang Yi, Xi Peng, Dezhong Peng"
] |
math.OC cs.LG stat.ML | 10.1109/CDC.2011.6160810 | 1304.6663 | null | null | http://arxiv.org/abs/1304.6663v2 | 2013-04-25T09:26:19Z | 2013-04-24T16:52:34Z | Low-rank optimization for distance matrix completion | This paper addresses the problem of low-rank distance matrix completion. This
problem amounts to recover the missing entries of a distance matrix when the
dimension of the data embedding space is possibly unknown but small compared to
the number of considered data points. The focus is on high-dimensional
problems. We recast the considered problem into an optimization problem over
the set of low-rank positive semidefinite matrices and propose two efficient
algorithms for low-rank distance matrix completion. In addition, we propose a
strategy to determine the dimension of the embedding space. The resulting
algorithms scale to high-dimensional problems and monotonically converge to a
global solution of the problem. Finally, numerical experiments illustrate the
good performance of the proposed algorithms on benchmarks.
| [
"['B. Mishra' 'G. Meyer' 'R. Sepulchre']",
"B. Mishra, G. Meyer and R. Sepulchre"
] |
cs.AI cs.LG cs.LO | 10.1017/S1471068414000076 | 1304.6810 | null | null | http://arxiv.org/abs/1304.6810v1 | 2013-04-25T06:10:55Z | 2013-04-25T06:10:55Z | Inference and learning in probabilistic logic programs using weighted
Boolean formulas | Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. This paper investigates how classical
inference and learning tasks known from the graphical model community can be
tackled for probabilistic logic programs. Several such tasks such as computing
the marginals given evidence and learning from (partial) interpretations have
not really been addressed for probabilistic logic programs before.
The first contribution of this paper is a suite of efficient algorithms for
various inference tasks. It is based on a conversion of the program and the
queries and evidence to a weighted Boolean formula. This allows us to reduce
the inference tasks to well-studied tasks such as weighted model counting,
which can be solved using state-of-the-art methods known from the graphical
model and knowledge compilation literature. The second contribution is an
algorithm for parameter estimation in the learning from interpretations
setting. The algorithm employs Expectation Maximization, and is built on top of
the developed inference algorithms.
The proposed approach is experimentally evaluated. The results show that the
inference algorithms improve upon the state-of-the-art in probabilistic logic
programming and that it is indeed possible to learn the parameters of a
probabilistic logic program from interpretations.
| [
"['Daan Fierens' 'Guy Van den Broeck' 'Joris Renkens' 'Dimitar Shterionov'\n 'Bernd Gutmann' 'Ingo Thon' 'Gerda Janssens' 'Luc De Raedt']",
"Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov,\n Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt"
] |
cs.LG cs.CV cs.MS | null | 1304.6899 | null | null | http://arxiv.org/pdf/1304.6899v1 | 2013-04-25T12:59:31Z | 2013-04-25T12:59:31Z | An implementation of the relational k-means algorithm | A C# implementation of a generalized k-means variant called relational
k-means is described here. Relational k-means is a generalization of the
well-known k-means clustering method which works for non-Euclidean scenarios as
well. The input is an arbitrary distance matrix, as opposed to the traditional
k-means method, where the clustered objects need to be identified with vectors.
| [
"['Balázs Szalkai']",
"Bal\\'azs Szalkai"
] |
cs.LG cs.AI stat.ML | null | 1304.7045 | null | null | http://arxiv.org/pdf/1304.7045v2 | 2014-02-20T13:14:59Z | 2013-04-26T00:35:37Z | An Algorithm for Training Polynomial Networks | We consider deep neural networks, in which the output of each node is a
quadratic function of its inputs. Similar to other deep architectures, these
networks can compactly represent any function on a finite training set. The
main goal of this paper is the derivation of an efficient layer-by-layer
algorithm for training such networks, which we denote as the \emph{Basis
Learner}. The algorithm is a universal learner in the sense that the training
error is guaranteed to decrease at every iteration, and can eventually reach
zero under mild conditions. We present practical implementations of this
algorithm, as well as preliminary experimental results. We also compare our
deep architecture to other shallow architectures for learning polynomials, in
particular kernel learning.
| [
"Roi Livni, Shai Shalev-Shwartz, Ohad Shamir",
"['Roi Livni' 'Shai Shalev-Shwartz' 'Ohad Shamir']"
] |
cs.LG | null | 1304.7158 | null | null | http://arxiv.org/pdf/1304.7158v1 | 2013-04-26T13:28:47Z | 2013-04-26T13:28:47Z | Irreflexive and Hierarchical Relations as Translations | We consider the problem of embedding entities and relations of knowledge
bases in low-dimensional vector spaces. Unlike most existing approaches, which
are primarily efficient for modeling equivalence relations, our approach is
designed to explicitly model irreflexive relations, such as hierarchies, by
interpreting them as translations operating on the low-dimensional embeddings
of the entities. Preliminary experiments show that, despite its simplicity and
a smaller number of parameters than previous approaches, our approach achieves
state-of-the-art performance according to standard evaluation protocols on data
from WordNet and Freebase.
| [
"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston\n and Oksana Yakhnenko",
"['Antoine Bordes' 'Nicolas Usunier' 'Alberto Garcia-Duran' 'Jason Weston'\n 'Oksana Yakhnenko']"
] |
stat.ML cs.LG | null | 1304.7230 | null | null | http://arxiv.org/pdf/1304.7230v2 | 2013-04-29T21:16:46Z | 2013-04-26T16:56:30Z | Learning Densities Conditional on Many Interacting Features | Learning a distribution conditional on a set of discrete-valued features is a
commonly encountered task. This becomes more challenging with a
high-dimensional feature set when there is the possibility of interaction
between the features. In addition, many frequently applied techniques consider
only prediction of the mean, but the complete conditional density is needed to
answer more complex questions. We demonstrate a novel nonparametric Bayes
method based upon a tensor factorization of feature-dependent weights for
Gaussian kernels. The method makes use of multistage feature selection for
dimension reduction. The resulting conditional density morphs flexibly with the
selected features.
| [
"David C. Kessler and Jack Taylor and David B. Dunson",
"['David C. Kessler' 'Jack Taylor' 'David B. Dunson']"
] |
cs.LG cs.CE stat.ML | null | 1304.7284 | null | null | http://arxiv.org/pdf/1304.7284v2 | 2013-10-16T07:04:04Z | 2013-04-26T20:47:46Z | Supervised Heterogeneous Multiview Learning for Joint Association Study
and Disease Diagnosis | Given genetic variations and various phenotypical traits, such as Magnetic
Resonance Imaging (MRI) features, we consider two important and related tasks
in biomedical research: i)to select genetic and phenotypical markers for
disease diagnosis and ii) to identify associations between genetic and
phenotypical data. These two tasks are tightly coupled because underlying
associations between genetic variations and phenotypical features contain the
biological basis for a disease. While a variety of sparse models have been
applied for disease diagnosis and canonical correlation analysis and its
extensions have bee widely used in association studies (e.g., eQTL analysis),
these two tasks have been treated separately. To unify these two tasks, we
present a new sparse Bayesian approach for joint association study and disease
diagnosis. In this approach, common latent features are extracted from
different data sources based on sparse projection matrices and used to predict
multiple disease severity levels based on Gaussian process ordinal regression;
in return, the disease status is used to guide the discovery of relationships
between the data sources. The sparse projection matrices not only reveal
interactions between data sources but also select groups of biomarkers related
to the disease. To learn the model from data, we develop an efficient
variational expectation maximization algorithm. Simulation results demonstrate
that our approach achieves higher accuracy in both predicting ordinal labels
and discovering associations between data sources than alternative methods. We
apply our approach to an imaging genetics dataset for the study of Alzheimer's
Disease (AD). Our method identifies biologically meaningful relationships
between genetic variations, MRI features, and AD status, and achieves
significantly higher accuracy for predicting ordinal AD stages than the
competing methods.
| [
"Shandian Zhe, Zenglin Xu, and Yuan Qi",
"['Shandian Zhe' 'Zenglin Xu' 'Yuan Qi']"
] |
cs.LG cs.CV | 10.1142/S0218001412500188 | 1304.7465 | null | null | http://arxiv.org/abs/1304.7465v1 | 2013-04-28T13:31:44Z | 2013-04-28T13:31:44Z | Deterministic Initialization of the K-Means Algorithm Using Hierarchical
Clustering | K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. Many of
these methods, however, have superlinear complexity in the number of data
points, making them impractical for large data sets. On the other hand, linear
methods are often random and/or order-sensitive, which renders their results
unrepeatable. Recently, Su and Dy proposed two highly successful hierarchical
initialization methods named Var-Part and PCA-Part that are not only linear,
but also deterministic (non-random) and order-invariant. In this paper, we
propose a discriminant analysis based approach that addresses a common
deficiency of these two methods. Experiments on a large and diverse collection
of data sets from the UCI Machine Learning Repository demonstrate that Var-Part
and PCA-Part are highly competitive with one of the best random initialization
methods to date, i.e., k-means++, and that the proposed approach significantly
improves the performance of both hierarchical methods.
| [
"M. Emre Celebi and Hassan A. Kingravi",
"['M. Emre Celebi' 'Hassan A. Kingravi']"
] |
cs.LG math.SP stat.ML | null | 1304.7528 | null | null | http://arxiv.org/pdf/1304.7528v1 | 2013-04-28T21:52:12Z | 2013-04-28T21:52:12Z | Semi-supervised Eigenvectors for Large-scale Locally-biased Learning | In many applications, one has side information, e.g., labels that are
provided in a semi-supervised manner, about a specific target region of a large
data set, and one wants to perform machine learning and data analysis tasks
"nearby" that prespecified target region. For example, one might be interested
in the clustering structure of a data graph near a prespecified "seed set" of
nodes, or one might be interested in finding partitions in an image that are
near a prespecified "ground truth" set of pixels. Locally-biased problems of
this sort are particularly challenging for popular eigenvector-based machine
learning and data analysis tools. At root, the reason is that eigenvectors are
inherently global quantities, thus limiting the applicability of
eigenvector-based methods in situations where one is interested in very local
properties of the data.
In this paper, we address this issue by providing a methodology to construct
semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these
locally-biased eigenvectors can be used to perform locally-biased machine
learning. These semi-supervised eigenvectors capture
successively-orthogonalized directions of maximum variance, conditioned on
being well-correlated with an input seed set of nodes that is assumed to be
provided in a semi-supervised manner. We show that these semi-supervised
eigenvectors can be computed quickly as the solution to a system of linear
equations; and we also describe several variants of our basic method that have
improved scaling properties. We provide several empirical examples
demonstrating how these semi-supervised eigenvectors can be used to perform
locally-biased learning; and we discuss the relationship between our results
and recent machine learning algorithms that use global eigenvectors of the
graph Laplacian.
| [
"['Toke J. Hansen' 'Michael W. Mahoney']",
"Toke J. Hansen and Michael W. Mahoney"
] |
cs.LG | null | 1304.7576 | null | null | http://arxiv.org/pdf/1304.7576v1 | 2013-04-29T07:16:54Z | 2013-04-29T07:16:54Z | Fractal structures in Adversarial Prediction | Fractals are self-similar recursive structures that have been used in
modeling several real world processes. In this work we study how "fractal-like"
processes arise in a prediction game where an adversary is generating a
sequence of bits and an algorithm is trying to predict them. We will see that
under a certain formalization of the predictive payoff for the algorithm it is
most optimal for the adversary to produce a fractal-like sequence to minimize
the algorithm's ability to predict. Indeed it has been suggested before that
financial markets exhibit a fractal-like behavior. We prove that a fractal-like
distribution arises naturally out of an optimization from the adversary's
perspective.
In addition, we give optimal trade-offs between predictability and expected
deviation (i.e. sum of bits) for our formalization of predictive payoff. This
result is motivated by the observation that several time series data exhibit
higher deviations than expected for a completely random walk.
| [
"['Rina Panigrahy' 'Preyas Popat']",
"Rina Panigrahy and Preyas Popat"
] |
cs.LG cs.DS stat.ML | null | 1304.7577 | null | null | http://arxiv.org/pdf/1304.7577v1 | 2013-04-29T07:17:31Z | 2013-04-29T07:17:31Z | Optimal amortized regret in every interval | Consider the classical problem of predicting the next bit in a sequence of
bits. A standard performance measure is {\em regret} (loss in payoff) with
respect to a set of experts. For example if we measure performance with respect
to two constant experts one that always predicts 0's and another that always
predicts 1's it is well known that one can get regret $O(\sqrt T)$ with respect
to the best expert by using, say, the weighted majority algorithm. But this
algorithm does not provide performance guarantee in any interval. There are
other algorithms that ensure regret $O(\sqrt {x \log T})$ in any interval of
length $x$. In this paper we show a randomized algorithm that in an amortized
sense gets a regret of $O(\sqrt x)$ for any interval when the sequence is
partitioned into intervals arbitrarily. We empirically estimated the constant
in the $O()$ for $T$ upto 2000 and found it to be small -- around 2.1. We also
experimentally evaluate the efficacy of this algorithm in predicting high
frequency stock data.
| [
"['Rina Panigrahy' 'Preyas Popat']",
"Rina Panigrahy and Preyas Popat"
] |
cs.SY cs.LG physics.soc-ph | null | 1304.7710 | null | null | http://arxiv.org/pdf/1304.7710v1 | 2013-04-29T16:48:02Z | 2013-04-29T16:48:02Z | Learning Geo-Temporal Non-Stationary Failure and Recovery of Power
Distribution | Smart energy grid is an emerging area for new applications of machine
learning in a non-stationary environment. Such a non-stationary environment
emerges when large-scale failures occur at power distribution networks due to
external disturbances such as hurricanes and severe storms. Power distribution
networks lie at the edge of the grid, and are especially vulnerable to external
disruptions. Quantifiable approaches are lacking and needed to learn
non-stationary behaviors of large-scale failure and recovery of power
distribution. This work studies such non-stationary behaviors in three aspects.
First, a novel formulation is derived for an entire life cycle of large-scale
failure and recovery of power distribution. Second, spatial-temporal models of
failure and recovery of power distribution are developed as geo-location based
multivariate non-stationary GI(t)/G(t)/Infinity queues. Third, the
non-stationary spatial-temporal models identify a small number of parameters to
be learned. Learning is applied to two real-life examples of large-scale
disruptions. One is from Hurricane Ike, where data from an operational network
is exact on failures and recoveries. The other is from Hurricane Sandy, where
aggregated data is used for inferring failure and recovery processes at one of
the impacted areas. Model parameters are learned using real data. Two findings
emerge as results of learning: (a) Failure rates behave similarly at the two
different provider networks for two different hurricanes but differently at the
geographical regions. (b) Both rapid- and slow-recovery are present for
Hurricane Ike but only slow recovery is shown for a regional distribution
network from Hurricane Sandy.
| [
"Yun Wei and Chuanyi Ji and Floyd Galvan and Stephen Couvillon and\n George Orellana and James Momoh",
"['Yun Wei' 'Chuanyi Ji' 'Floyd Galvan' 'Stephen Couvillon'\n 'George Orellana' 'James Momoh']"
] |
cs.LG cs.SD | null | 1304.7851 | null | null | http://arxiv.org/pdf/1304.7851v2 | 2013-06-07T02:01:07Z | 2013-04-30T03:41:14Z | North Atlantic Right Whale Contact Call Detection | The North Atlantic right whale (Eubalaena glacialis) is an endangered
species. These whales continuously suffer from deadly vessel impacts alongside
the eastern coast of North America. There have been countless efforts to save
the remaining 350 - 400 of them. One of the most prominent works is done by
Marinexplore and Cornell University. A system of hydrophones linked to
satellite connected-buoys has been deployed in the whales habitat. These
hydrophones record and transmit live sounds to a base station. These recording
might contain the right whale contact call as well as many other noises. The
noise rate increases rapidly in vessel-busy areas such as by the Boston harbor.
This paper presents and studies the problem of detecting the North Atlantic
right whale contact call with the presence of noise and other marine life
sounds. A novel algorithm was developed to preprocess the sound waves before a
tree based hierarchical classifier is used to classify the data and provide a
score. The developed model was trained with 30,000 data points made available
through the Cornell University Whale Detection Challenge program. Results
showed that the developed algorithm had close to 85% success rate in detecting
the presence of the North Atlantic right whale.
| [
"['Rami Abousleiman' 'Guangzhi Qu' 'Osamah Rawashdeh']",
"Rami Abousleiman, Guangzhi Qu, Osamah Rawashdeh"
] |
cs.LG stat.ML | null | 1304.8020 | null | null | http://arxiv.org/pdf/1304.8020v2 | 2013-05-01T11:53:05Z | 2013-04-30T14:59:49Z | Semi-Supervised Information-Maximization Clustering | Semi-supervised clustering aims to introduce prior knowledge in the decision
process of a clustering algorithm. In this paper, we propose a novel
semi-supervised clustering algorithm based on the information-maximization
principle. The proposed method is an extension of a previous unsupervised
information-maximization clustering algorithm based on squared-loss mutual
information to effectively incorporate must-links and cannot-links. The
proposed method is computationally efficient because the clustering solution
can be obtained analytically via eigendecomposition. Furthermore, the proposed
method allows systematic optimization of tuning parameters such as the kernel
width, given the degree of belief in the must-links and cannot-links. The
usefulness of the proposed method is demonstrated through experiments.
| [
"Daniele Calandriello, Gang Niu, Masashi Sugiyama",
"['Daniele Calandriello' 'Gang Niu' 'Masashi Sugiyama']"
] |
cs.DS cs.LG math.ST stat.TH | null | 1304.8087 | null | null | http://arxiv.org/pdf/1304.8087v1 | 2013-04-30T17:35:37Z | 2013-04-30T17:35:37Z | Uniqueness of Tensor Decompositions with Applications to Polynomial
Identifiability | We give a robust version of the celebrated result of Kruskal on the
uniqueness of tensor decompositions: we prove that given a tensor whose
decomposition satisfies a robust form of Kruskal's rank condition, it is
possible to approximately recover the decomposition if the tensor is known up
to a sufficiently small (inverse polynomial) error.
Kruskal's theorem has found many applications in proving the identifiability
of parameters for various latent variable models and mixture models such as
Hidden Markov models, topic models etc. Our robust version immediately implies
identifiability using only polynomially many samples in many of these settings.
This polynomial identifiability is an essential first step towards efficient
learning algorithms for these models.
Recently, algorithms based on tensor decompositions have been used to
estimate the parameters of various hidden variable models efficiently in
special cases as long as they satisfy certain "non-degeneracy" properties. Our
methods give a way to go beyond this non-degeneracy barrier, and establish
polynomial identifiability of the parameters under much milder conditions.
Given the importance of Kruskal's theorem in the tensor literature, we expect
that this robust version will have several applications beyond the settings we
explore in this work.
| [
"['Aditya Bhaskara' 'Moses Charikar' 'Aravindan Vijayaraghavan']",
"Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan"
] |
null | null | 1304.8132 | null | null | http://arxiv.org/pdf/1304.8132v2 | 2013-11-07T18:25:15Z | 2013-04-30T19:57:36Z | Local Graph Clustering Beyond Cheeger's Inequality | Motivated by applications of large-scale graph clustering, we study random-walk-based LOCAL algorithms whose running times depend only on the size of the output cluster, rather than the entire graph. All previously known such algorithms guarantee an output conductance of $tilde{O}(sqrt{phi(A)})$ when the target set $A$ has conductance $phi(A)in[0,1]$. In this paper, we improve it to $$tilde{O}bigg( minBig{sqrt{phi(A)}, frac{phi(A)}{sqrt{mathsf{Conn}(A)}} Big} bigg)enspace, $$ where the internal connectivity parameter $mathsf{Conn}(A) in [0,1]$ is defined as the reciprocal of the mixing time of the random walk over the induced subgraph on $A$. For instance, using $mathsf{Conn}(A) = Omega(lambda(A) / log n)$ where $lambda$ is the second eigenvalue of the Laplacian of the induced subgraph on $A$, our conductance guarantee can be as good as $tilde{O}(phi(A)/sqrt{lambda(A)})$. This builds an interesting connection to the recent advance of the so-called improved Cheeger's Inequality [KKL+13], which says that global spectral algorithms can provide a conductance guarantee of $O(phi_{mathsf{opt}}/sqrt{lambda_3})$ instead of $O(sqrt{phi_{mathsf{opt}}})$. In addition, we provide theoretical guarantee on the clustering accuracy (in terms of precision and recall) of the output set. We also prove that our analysis is tight, and perform empirical evaluation to support our theory on both synthetic and real data. It is worth noting that, our analysis outperforms prior work when the cluster is well-connected. In fact, the better it is well-connected inside, the more significant improvement (both in terms of conductance and accuracy) we can obtain. Our results shed light on why in practice some random-walk-based algorithms perform better than its previous theory, and help guide future research about local clustering. | [
"['Zeyuan Allen Zhu' 'Silvio Lattanzi' 'Vahab Mirrokni']"
] |
stat.ML cs.LG | null | 1305.0015 | null | null | http://arxiv.org/pdf/1305.0015v1 | 2013-04-30T20:12:01Z | 2013-04-30T20:12:01Z | Inferring ground truth from multi-annotator ordinal data: a
probabilistic approach | A popular approach for large scale data annotation tasks is crowdsourcing,
wherein each data point is labeled by multiple noisy annotators. We consider
the problem of inferring ground truth from noisy ordinal labels obtained from
multiple annotators of varying and unknown expertise levels. Annotation models
for ordinal data have been proposed mostly as extensions of their
binary/categorical counterparts and have received little attention in the
crowdsourcing literature. We propose a new model for crowdsourced ordinal data
that accounts for instance difficulty as well as annotator expertise, and
derive a variational Bayesian inference algorithm for parameter estimation. We
analyze the ordinal extensions of several state-of-the-art annotator models for
binary/categorical labels and evaluate the performance of all the models on two
real world datasets containing ordinal query-URL relevance scores, collected
through Amazon's Mechanical Turk. Our results indicate that the proposed model
performs better or as well as existing state-of-the-art methods and is more
resistant to `spammy' annotators (i.e., annotators who assign labels randomly
without actually looking at the instance) than popular baselines such as mean,
median, and majority vote which do not account for annotator expertise.
| [
"Balaji Lakshminarayanan and Yee Whye Teh",
"['Balaji Lakshminarayanan' 'Yee Whye Teh']"
] |
cs.SI cs.LG physics.soc-ph stat.ML | 10.1109/ICC.2009.5199418 | 1305.0051 | null | null | http://arxiv.org/abs/1305.0051v1 | 2013-04-30T22:57:12Z | 2013-04-30T22:57:12Z | Revealing social networks of spammers through spectral clustering | To date, most studies on spam have focused only on the spamming phase of the
spam cycle and have ignored the harvesting phase, which consists of the mass
acquisition of email addresses. It has been observed that spammers conceal
their identity to a lesser degree in the harvesting phase, so it may be
possible to gain new insights into spammers' behavior by studying the behavior
of harvesters, which are individuals or bots that collect email addresses. In
this paper, we reveal social networks of spammers by identifying communities of
harvesters with high behavioral similarity using spectral clustering. The data
analyzed was collected through Project Honey Pot, a distributed system for
monitoring harvesting and spamming. Our main findings are (1) that most
spammers either send only phishing emails or no phishing emails at all, (2)
that most communities of spammers also send only phishing emails or no phishing
emails at all, and (3) that several groups of spammers within communities
exhibit coherent temporal behavior and have similar IP addresses. Our findings
reveal some previously unknown behavior of spammers and suggest that there is
indeed social structure between spammers to be discovered.
| [
"Kevin S. Xu, Mark Kliger, Yilun Chen, Peter J. Woolf, and Alfred O.\n Hero III",
"['Kevin S. Xu' 'Mark Kliger' 'Yilun Chen' 'Peter J. Woolf'\n 'Alfred O. Hero III']"
] |
cs.LG | null | 1305.0103 | null | null | http://arxiv.org/pdf/1305.0103v1 | 2013-05-01T06:32:12Z | 2013-05-01T06:32:12Z | Clustering Unclustered Data: Unsupervised Binary Labeling of Two
Datasets Having Different Class Balances | We consider the unsupervised learning problem of assigning labels to
unlabeled data. A naive approach is to use clustering methods, but this works
well only when data is properly clustered and each cluster corresponds to an
underlying class. In this paper, we first show that this unsupervised labeling
problem in balanced binary cases can be solved if two unlabeled datasets having
different class balances are available. More specifically, estimation of the
sign of the difference between probability densities of two unlabeled datasets
gives the solution. We then introduce a new method to directly estimate the
sign of the density difference without density estimation. Finally, we
demonstrate the usefulness of the proposed method against several clustering
methods on various toy problems and real-world datasets.
| [
"['Marthinus Christoffel du Plessis' 'Masashi Sugiyama']",
"Marthinus Christoffel du Plessis and Masashi Sugiyama"
] |
cs.LG | null | 1305.0208 | null | null | http://arxiv.org/pdf/1305.0208v2 | 2013-07-23T02:13:57Z | 2013-05-01T15:45:34Z | Perceptron Mistake Bounds | We present a brief survey of existing mistake bounds and introduce novel
bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds
generalize beyond standard margin-loss type bounds, allow for any convex and
Lipschitz loss function, and admit a very simple proof.
| [
"Mehryar Mohri, Afshin Rostamizadeh",
"['Mehryar Mohri' 'Afshin Rostamizadeh']"
] |
math.ST cs.IT cs.LG math.IT stat.ME stat.ML stat.TH | null | 1305.0355 | null | null | http://arxiv.org/pdf/1305.0355v1 | 2013-05-02T07:25:52Z | 2013-05-02T07:25:52Z | Model Selection for High-Dimensional Regression under the Generalized
Irrepresentability Condition | In the high-dimensional regression model a response variable is linearly
related to $p$ covariates, but the sample size $n$ is smaller than $p$. We
assume that only a small subset of covariates is `active' (i.e., the
corresponding coefficients are non-zero), and consider the model-selection
problem of identifying the active covariates. A popular approach is to estimate
the regression coefficients through the Lasso ($\ell_1$-regularized least
squares). This is known to correctly identify the active set only if the
irrelevant covariates are roughly orthogonal to the relevant ones, as
quantified through the so called `irrepresentability' condition. In this paper
we study the `Gauss-Lasso' selector, a simple two-stage method that first
solves the Lasso, and then performs ordinary least squares restricted to the
Lasso active set. We formulate `generalized irrepresentability condition'
(GIC), an assumption that is substantially weaker than irrepresentability. We
prove that, under GIC, the Gauss-Lasso correctly recovers the active set.
| [
"['Adel Javanmard' 'Andrea Montanari']",
"Adel Javanmard and Andrea Montanari"
] |
cs.NA cs.LG q-bio.NC stat.ML | null | 1305.0395 | null | null | http://arxiv.org/pdf/1305.0395v1 | 2013-05-02T11:17:47Z | 2013-05-02T11:17:47Z | Tensor Decompositions: A New Concept in Brain Data Analysis? | Matrix factorizations and their extensions to tensor factorizations and
decompositions have become prominent techniques for linear and multilinear
blind source separation (BSS), especially multiway Independent Component
Analysis (ICA), NonnegativeMatrix and Tensor Factorization (NMF/NTF), Smooth
Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover,
tensor decompositions have many other potential applications beyond multilinear
BSS, especially feature extraction, classification, dimensionality reduction
and multiway clustering. In this paper, we briefly overview new and emerging
models and approaches for tensor decompositions in applications to group and
linked multiway BSS/ICA, feature extraction, classification andMultiway Partial
Least Squares (MPLS) regression problems. Keywords: Multilinear BSS, linked
multiway BSS/ICA, tensor factorizations and decompositions, constrained Tucker
and CP models, Penalized Tensor Decompositions (PTD), feature extraction,
classification, multiway PLS and CCA.
| [
"Andrzej Cichocki",
"['Andrzej Cichocki']"
] |
cs.LG cs.AI stat.ML | null | 1305.0423 | null | null | http://arxiv.org/pdf/1305.0423v1 | 2013-05-02T13:03:53Z | 2013-05-02T13:03:53Z | Testing Hypotheses by Regularized Maximum Mean Discrepancy | Do two data samples come from different distributions? Recent studies of this
fundamental problem focused on embedding probability distributions into
sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to
compare distributions by the distance between their embeddings. We show that
Regularized Maximum Mean Discrepancy (RMMD), our novel measure for kernel-based
hypothesis testing, yields substantial improvements even when sample sizes are
small, and excels at hypothesis tests involving multiple comparisons with power
control. We derive asymptotic distributions under the null and alternative
hypotheses, and assess power control. Outstanding results are obtained on:
challenging EEG data, MNIST, the Berkley Covertype, and the Flare-Solar
dataset.
| [
"Somayeh Danafar, Paola M.V. Rancoita, Tobias Glasmachers, Kevin\n Whittingstall, Juergen Schmidhuber",
"['Somayeh Danafar' 'Paola M. V. Rancoita' 'Tobias Glasmachers'\n 'Kevin Whittingstall' 'Juergen Schmidhuber']"
] |
cs.LG | null | 1305.0445 | null | null | http://arxiv.org/pdf/1305.0445v2 | 2013-06-07T02:35:21Z | 2013-05-02T14:33:28Z | Deep Learning of Representations: Looking Forward | Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges.
| [
"['Yoshua Bengio']",
"Yoshua Bengio"
] |
cs.LG cs.AI stat.ML | null | 1305.0626 | null | null | http://arxiv.org/pdf/1305.0626v1 | 2013-05-03T06:25:41Z | 2013-05-03T06:25:41Z | An Improved EM algorithm | In this paper, we firstly give a brief introduction of expectation
maximization (EM) algorithm, and then discuss the initial value sensitivity of
expectation maximization algorithm. Subsequently, we give a short proof of EM's
convergence. Then, we implement experiments with the expectation maximization
algorithm (We implement all the experiments on Gaussion mixture model (GMM)).
Our experiment with expectation maximization is performed in the following
three cases: initialize randomly; initialize with result of K-means; initialize
with result of K-medoids. The experiment result shows that expectation
maximization algorithm depend on its initial state or parameters. And we found
that EM initialized with K-medoids performed better than both the one
initialized with K-means and the one initialized randomly.
| [
"Fuqiang Chen",
"['Fuqiang Chen']"
] |
cs.LG cs.IR stat.ML | null | 1305.0638 | null | null | http://arxiv.org/pdf/1305.0638v1 | 2013-05-03T08:26:05Z | 2013-05-03T08:26:05Z | Feature Selection Based on Term Frequency and T-Test for Text
Categorization | Much work has been done on feature selection. Existing methods are based on
document frequency, such as Chi-Square Statistic, Information Gain etc.
However, these methods have two shortcomings: one is that they are not reliable
for low-frequency terms, and the other is that they only count whether one term
occurs in a document and ignore the term frequency. Actually, high-frequency
terms within a specific category are often regards as discriminators.
This paper focuses on how to construct the feature selection function based
on term frequency, and proposes a new approach based on $t$-test, which is used
to measure the diversity of the distributions of a term between the specific
category and the entire corpus. Extensive comparative experiments on two text
corpora using three classifiers show that our new approach is comparable to or
or slightly better than the state-of-the-art feature selection methods (i.e.,
$\chi^2$, and IG) in terms of macro-$F_1$ and micro-$F_1$.
| [
"['Deqing Wang' 'Hui Zhang' 'Rui Liu' 'Weifeng Lv']",
"Deqing Wang, Hui Zhang, Rui Liu, Weifeng Lv"
] |
cs.LG | null | 1305.0665 | null | null | http://arxiv.org/pdf/1305.0665v2 | 2013-10-13T01:03:56Z | 2013-05-03T10:20:02Z | Spectral Classification Using Restricted Boltzmann Machine | In this study, a novel machine learning algorithm, restricted Boltzmann
machine (RBM), is introduced. The algorithm is applied for the spectral
classification in astronomy. RBM is a bipartite generative graphical model with
two separate layers (one visible layer and one hidden layer), which can extract
higher level features to represent the original data. Despite generative, RBM
can be used for classification when modified with a free energy and a soft-max
function. Before spectral classification, the original data is binarized
according to some rule. Then we resort to the binary RBM to classify
cataclysmic variables (CVs) and non-CVs (one half of all the given data for
training and the other half for testing). The experiment result shows
state-of-the-art accuracy of 100%, which indicates the efficiency of the binary
RBM algorithm.
| [
"['Fuqiang Chen' 'Yan Wu' 'Yude Bu' 'Guodong Zhao']",
"Fuqiang Chen, Yan Wu, Yude Bu, Guodong Zhao"
] |
cs.LG | null | 1305.0698 | null | null | http://arxiv.org/pdf/1305.0698v1 | 2013-05-03T13:26:24Z | 2013-05-03T13:26:24Z | Learning from Imprecise and Fuzzy Observations: Data Disambiguation
through Generalized Loss Minimization | Methods for analyzing or learning from "fuzzy data" have attracted increasing
attention in recent years. In many cases, however, existing methods (for
precise, non-fuzzy data) are extended to the fuzzy case in an ad-hoc manner,
and without carefully considering the interpretation of a fuzzy set when being
used for modeling data. Distinguishing between an ontic and an epistemic
interpretation of fuzzy set-valued data, and focusing on the latter, we argue
that a "fuzzification" of learning algorithms based on an application of the
generic extension principle is not appropriate. In fact, the extension
principle fails to properly exploit the inductive bias underlying statistical
and machine learning methods, although this bias, at least in principle, offers
a means for "disambiguating" the fuzzy data. Alternatively, we therefore
propose a method which is based on the generalization of loss functions in
empirical risk minimization, and which performs model identification and data
disambiguation simultaneously. Elaborating on the fuzzification of specific
types of losses, we establish connections to well-known loss functions in
regression and classification. We compare our approach with related methods and
illustrate its use in logistic regression for binary classification.
| [
"['Eyke Hüllermeier']",
"Eyke H\\\"ullermeier"
] |
cs.NE cs.LG | null | 1305.0922 | null | null | http://arxiv.org/pdf/1305.0922v1 | 2013-05-04T14:06:48Z | 2013-05-04T14:06:48Z | On Comparison between Evolutionary Programming Network-based Learning
and Novel Evolution Strategy Algorithm-based Learning | This paper presents two different evolutionary systems - Evolutionary
Programming Network (EPNet) and Novel Evolutions Strategy (NES) Algorithm.
EPNet does both training and architecture evolution simultaneously, whereas NES
does a fixed network and only trains the network. Five mutation operators
proposed in EPNet to reflect the emphasis on evolving ANNs behaviors. Close
behavioral links between parents and their offspring are maintained by various
mutations, such as partial training and node splitting. On the other hand, NES
uses two new genetic operators - subpopulation-based max-mean arithmetical
crossover and time-variant mutation. The above-mentioned two algorithms have
been tested on a number of benchmark problems, such as the medical diagnosis
problems (breast cancer, diabetes, and heart disease). The results and the
comparison between them are also presented in this paper.
| [
"M.A. Khayer Azad, Md. Shafiqul Islam and M.M.A. Hashem",
"['M. A. Khayer Azad' 'Md. Shafiqul Islam' 'M. M. A. Hashem']"
] |
cs.LG stat.ML | null | 1305.1002 | null | null | http://arxiv.org/pdf/1305.1002v1 | 2013-05-05T09:44:08Z | 2013-05-05T09:44:08Z | Efficient Estimation of the number of neighbours in Probabilistic K
Nearest Neighbour Classification | Probabilistic k-nearest neighbour (PKNN) classification has been introduced
to improve the performance of original k-nearest neighbour (KNN) classification
algorithm by explicitly modelling uncertainty in the classification of each
feature vector. However, an issue common to both KNN and PKNN is to select the
optimal number of neighbours, $k$. The contribution of this paper is to
incorporate the uncertainty in $k$ into the decision making, and in so doing
use Bayesian model averaging to provide improved classification. Indeed the
problem of assessing the uncertainty in $k$ can be viewed as one of statistical
model selection which is one of the most important technical issues in the
statistics and machine learning domain. In this paper, a new functional
approximation algorithm is proposed to reconstruct the density of the model
(order) without relying on time consuming Monte Carlo simulations. In addition,
this algorithm avoids cross validation by adopting Bayesian framework. The
performance of this algorithm yielded very good performance on several real
experimental datasets.
| [
"Ji Won Yoon and Nial Friel",
"['Ji Won Yoon' 'Nial Friel']"
] |
cs.LG | null | 1305.1019 | null | null | http://arxiv.org/pdf/1305.1019v2 | 2014-01-02T23:37:26Z | 2013-05-05T14:58:15Z | Simple Deep Random Model Ensemble | Representation learning and unsupervised learning are two central topics of
machine learning and signal processing. Deep learning is one of the most
effective unsupervised representation learning approach. The main contributions
of this paper to the topics are as follows. (i) We propose to view the
representative deep learning approaches as special cases of the knowledge reuse
framework of clustering ensemble. (ii) We propose to view sparse coding when
used as a feature encoder as the consensus function of clustering ensemble, and
view dictionary learning as the training process of the base clusterings of
clustering ensemble. (ii) Based on the above two views, we propose a very
simple deep learning algorithm, named deep random model ensemble (DRME). It is
a stack of random model ensembles. Each random model ensemble is a special
k-means ensemble that discards the expectation-maximization optimization of
each base k-means but only preserves the default initialization method of the
base k-means. (iv) We propose to select the most powerful representation among
the layers by applying DRME to clustering where the single-linkage is used as
the clustering algorithm. Moreover, the DRME based clustering can also detect
the number of the natural clusters accurately. Extensive experimental
comparisons with 5 representation learning methods on 19 benchmark data sets
demonstrate the effectiveness of DRME.
| [
"['Xiao-Lei Zhang' 'Ji Wu']",
"Xiao-Lei Zhang, Ji Wu"
] |
stat.ML cs.LG | null | 1305.1027 | null | null | http://arxiv.org/pdf/1305.1027v2 | 2013-07-17T21:07:37Z | 2013-05-05T16:59:58Z | Regret Bounds for Reinforcement Learning with Policy Advice | In some reinforcement learning problems an agent may be provided with a set
of input policies, perhaps learned from prior experience or provided by
advisors. We present a reinforcement learning with policy advice (RLPA)
algorithm which leverages this input set and learns to use the best policy in
the set for the reinforcement learning task at hand. We prove that RLPA has a
sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and
that both this regret and its computational complexity are independent of the
size of the state and action space. Our empirical simulations support our
theoretical analysis. This suggests RLPA may offer significant advantages in
large domains where some prior good policies are provided.
| [
"Mohammad Gheshlaghi Azar and Alessandro Lazaric and Emma Brunskill",
"['Mohammad Gheshlaghi Azar' 'Alessandro Lazaric' 'Emma Brunskill']"
] |
stat.ML cs.LG | null | 1305.1040 | null | null | http://arxiv.org/pdf/1305.1040v1 | 2013-05-05T18:51:24Z | 2013-05-05T18:51:24Z | On the Convergence and Consistency of the Blurring Mean-Shift Process | The mean-shift algorithm is a popular algorithm in computer vision and image
processing. It can also be cast as a minimum gamma-divergence estimation. In
this paper we focus on the "blurring" mean shift algorithm, which is one
version of the mean-shift process that successively blurs the dataset. The
analysis of the blurring mean-shift is relatively more complicated compared to
the nonblurring version, yet the algorithm convergence and the estimation
consistency have not been well studied in the literature. In this paper we
prove both the convergence and the consistency of the blurring mean-shift. We
also perform simulation studies to compare the efficiency of the blurring and
the nonblurring versions of the mean-shift algorithms. Our results show that
the blurring mean-shift has more efficiency.
| [
"Ting-Li Chen",
"['Ting-Li Chen']"
] |
cs.CG cs.LG math.MG | null | 1305.1172 | null | null | http://arxiv.org/pdf/1305.1172v1 | 2013-05-06T12:57:24Z | 2013-05-06T12:57:24Z | Gromov-Hausdorff Approximation of Metric Spaces with Linear Structure | In many real-world applications data come as discrete metric spaces sampled
around 1-dimensional filamentary structures that can be seen as metric graphs.
In this paper we address the metric reconstruction problem of such filamentary
structures from data sampled around them. We prove that they can be
approximated, with respect to the Gromov-Hausdorff distance by well-chosen Reeb
graphs (and some of their variants) and we provide an efficient and easy to
implement algorithm to compute such approximations in almost linear time. We
illustrate the performances of our algorithm on a few synthetic and real data
sets.
| [
"Fr\\'ed\\'eric Chazal (INRIA Sophia Antipolis / INRIA Saclay - Ile de\n France), Jian Sun",
"['Frédéric Chazal' 'Jian Sun']"
] |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.