categories
string
doi
string
id
string
year
float64
venue
string
link
string
updated
string
published
string
title
string
abstract
string
authors
list
cs.LG cs.CY
null
1311.6802
null
null
http://arxiv.org/pdf/1311.6802v2
2014-07-30T23:08:54Z
2013-11-26T20:48:59Z
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization
Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that solicit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.
[ "Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Nina Taft", "['Smriti Bhagat' 'Udi Weinsberg' 'Stratis Ioannidis' 'Nina Taft']" ]
cs.LG
10.1109/TSP.2014.2333559
1311.6809
null
null
http://arxiv.org/abs/1311.6809v1
2013-11-26T10:02:20Z
2013-11-26T10:02:20Z
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce important members of this family of algorithms such as the least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD) algorithms that improve the convergence performance of the conventional algorithms. However, our approach and analysis are generic such that they cover other well-known cost functions as described in the paper. The LMLS algorithm achieves comparable convergence performance with the least mean fourth (LMF) algorithm and extends the stability bound on the step size. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interferences and outperforms the sign algorithm (SA). We analyze the transient, steady state and tracking performance of the introduced algorithms and demonstrate the match of the theoretical analyzes and simulation results. We show the extended stability bound of the LMLS algorithm and analyze the robustness of the LLAD algorithm against impulsive interferences. Finally, we demonstrate the performance of our algorithms in different scenarios through numerical examples.
[ "Muhammed O. Sayin, N. Denizcan Vanli, Suleyman S. Kozat", "['Muhammed O. Sayin' 'N. Denizcan Vanli' 'Suleyman S. Kozat']" ]
stat.ML cs.LG
10.1109/IJCNN.2014.6889449
1311.6834
null
null
http://arxiv.org/abs/1311.6834v2
2015-01-16T18:37:00Z
2013-11-26T22:13:37Z
Semi-Supervised Sparse Coding
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.
[ "Jim Jing-Yan Wang and Xin Gao", "['Jim Jing-Yan Wang' 'Xin Gao']" ]
cs.LG cs.GT
null
1311.6838
null
null
http://arxiv.org/pdf/1311.6838v1
2013-11-26T22:53:13Z
2013-11-26T22:53:13Z
Learning Prices for Repeated Auctions with Strategic Buyers
Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.
[ "['Kareem Amin' 'Afshin Rostamizadeh' 'Umar Syed']", "Kareem Amin, Afshin Rostamizadeh, Umar Syed" ]
cs.CV cs.LG cs.NE
null
1311.6881
null
null
http://arxiv.org/pdf/1311.6881v1
2013-11-27T07:14:25Z
2013-11-27T07:14:25Z
Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey
The improvement of the accuracy of image query retrieval used image classification technique. Image classification is well known technique of supervised learning. The improved method of image classification increases the working efficiency of image query retrieval. For the improvements of classification technique we used RBF neural network function for better prediction of feature used in image retrieval.Colour content is represented by pixel values in image classification using radial base function(RBF) technique. This approach provides better result compare to SVM technique in image representation.Image is represented by matrix though RBF using pixel values of colour intensity of image. Firstly we using RGB colour model. In this colour model we use red, green and blue colour intensity values in matrix.SVM with partical swarm optimization for image classification is implemented in content of images which provide better Results based on the proposed approach are found encouraging in terms of color image classification accuracy.
[ "['Abhishek Pandey' 'Anjna Jayant Deen' 'Rajeev Pandey']", "Abhishek Pandey, Anjna Jayant Deen and Rajeev Pandey (Dept. of CSE,\n UIT-RGPV)" ]
stat.ML cs.LG stat.AP stat.ME
null
1311.6976
null
null
http://arxiv.org/pdf/1311.6976v2
2014-05-13T09:21:28Z
2013-11-27T14:19:21Z
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation
In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickthrough rate prediction need to be sufficiently fast. In this work, we propose to use dimensionality reduction of the user-website interaction graph in order to produce simplified features of users and websites that can be used as predictors of clickthrough rate. We demonstrate that the Infinite Relational Model (IRM) as a dimensionality reduction offers comparable predictive performance to conventional dimensionality reduction schemes, while achieving the most economical usage of features and fastest computations at run-time. For applications such as real-time bidding, where fast database I/O and few computations are key to success, we thus recommend using IRM based features as predictors to exploit the recommender effects from bipartite graphs.
[ "['Bjarne Ørum Fruergaard' 'Toke Jansen Hansen' 'Lars Kai Hansen']", "Bjarne {\\O}rum Fruergaard, Toke Jansen Hansen, Lars Kai Hansen" ]
cs.AI cs.LG stat.ML
null
1311.7071
null
null
http://arxiv.org/pdf/1311.7071v2
2013-12-03T20:08:28Z
2013-11-27T18:58:07Z
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series. However, in general, it is difficult to set the dimension of its hidden state space. A small number of hidden states may not be able to model the complexities of a time series, while a large number of hidden states can lead to overfitting. In this paper, we study methods that impose an $\ell_1$ regularization on the transition matrix of an LDS model to alleviate the problem of choosing the optimal number of hidden states. We incorporate a generalized gradient descent method into the Maximum a Posteriori (MAP) framework and use Expectation Maximization (EM) to iteratively achieve sparsity on the transition matrix of an LDS model. We show that our Sparse Linear Dynamical System (SLDS) improves the predictive performance when compared to ordinary LDS on a multivariate clinical time series dataset.
[ "Zitao Liu and Milos Hauskrecht", "['Zitao Liu' 'Milos Hauskrecht']" ]
stat.ML cs.LG
null
1311.7184
null
null
http://arxiv.org/pdf/1311.7184v1
2013-11-28T01:36:49Z
2013-11-28T01:36:49Z
Using Multiple Samples to Learn Mixture Models
In the mixture models problem it is assumed that there are $K$ distributions $\theta_{1},\ldots,\theta_{K}$ and one gets to observe a sample from a mixture of these distributions with unknown coefficients. The goal is to associate instances with their generating distributions, or to identify the parameters of the hidden distributions. In this work we make the assumption that we have access to several samples drawn from the same $K$ underlying distributions, but with different mixing weights. As with topic modeling, having multiple samples is often a reasonable assumption. Instead of pooling the data into one sample, we prove that it is possible to use the differences between the samples to better recover the underlying structure. We present algorithms that recover the underlying structure under milder assumptions than the current state of art when either the dimensionality or the separation is high. The methods, when applied to topic modeling, allow generalization to words not present in the training data.
[ "['Jason D Lee' 'Ran Gilad-Bachrach' 'Rich Caruana']", "Jason D Lee, Ran Gilad-Bachrach, and Rich Caruana" ]
cs.LG math.OC stat.ML
null
1311.7198
null
null
http://arxiv.org/pdf/1311.7198v1
2013-11-28T03:59:31Z
2013-11-28T03:59:31Z
ADMM Algorithm for Graphical Lasso with an $\ell_{\infty}$ Element-wise Norm Constraint
We consider the problem of Graphical lasso with an additional $\ell_{\infty}$ element-wise norm constraint on the precision matrix. This problem has applications in high-dimensional covariance decomposition such as in \citep{Janzamin-12}. We propose an ADMM algorithm to solve this problem. We also use a continuation strategy on the penalty parameter to have a fast implemenation of the algorithm.
[ "Karthik Mohan", "['Karthik Mohan']" ]
cs.CV cs.LG cs.NE
null
1311.7251
null
null
http://arxiv.org/pdf/1311.7251v1
2013-11-28T09:44:45Z
2013-11-28T09:44:45Z
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by feed-forward neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.
[ "['Joseph Shtok' 'Michael Zibulevsky' 'Michael Elad']", "Joseph Shtok, Michael Zibulevsky and Michael Elad" ]
cs.LG
null
1311.7385
null
null
http://arxiv.org/pdf/1311.7385v3
2014-07-11T17:10:27Z
2013-11-28T17:44:45Z
Algorithmic Identification of Probabilities
TThe problem is to identify a probability associated with a set of natural numbers, given an infinite data sequence of elements from the set. If the given sequence is drawn i.i.d. and the probability mass function involved (the target) belongs to a computably enumerable (c.e.) or co-computably enumerable (co-c.e.) set of computable probability mass functions, then there is an algorithm to almost surely identify the target in the limit. The technical tool is the strong law of large numbers. If the set is finite and the elements of the sequence are dependent while the sequence is typical in the sense of Martin-L\"of for at least one measure belonging to a c.e. or co-c.e. set of computable measures, then there is an algorithm to identify in the limit a computable measure for which the sequence is typical (there may be more than one such measure). The technical tool is the theory of Kolmogorov complexity. We give the algorithms and consider the associated predictions.
[ "Paul M.B. Vitanyi (CWI and University of Amsterdam, NL), Nick Chater\n (University of Warwick, UK)", "['Paul M. B. Vitanyi' 'Nick Chater']" ]
cs.LG cs.CV cs.IR
null
1311.7662
null
null
http://arxiv.org/pdf/1311.7662v1
2013-11-29T18:53:32Z
2013-11-29T18:53:32Z
The Power of Asymmetry in Binary Hashing
When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps. I.e. by approximating the similarity between $x$ and $x'$ as the hamming distance between $f(x)$ and $g(x')$, for two distinct binary codes $f,g$, rather than as the hamming distance between $f(x)$ and $f(x')$.
[ "['Behnam Neyshabur' 'Payman Yadollahpour' 'Yury Makarychev'\n 'Ruslan Salakhutdinov' 'Nathan Srebro']", "Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan\n Salakhutdinov, Nathan Srebro" ]
cs.LG
null
1311.7679
null
null
http://arxiv.org/pdf/1311.7679v1
2013-11-29T20:01:10Z
2013-11-29T20:01:10Z
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team "binghsu & MLRush & BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.
[ "Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li,\n Hanxiao Sun, Bin Wang", "['Xudong Liu' 'Bing Xu' 'Yuyu Zhang' 'Qiang Yan' 'Liang Pang' 'Qiang Li'\n 'Hanxiao Sun' 'Bin Wang']" ]
cs.LG
null
1312.0048
null
null
http://arxiv.org/pdf/1312.0048v1
2013-11-30T01:07:25Z
2013-11-30T01:07:25Z
Stochastic Optimization of Smooth Loss
In this paper, we first prove a high probability bound rather than an expectation bound for stochastic optimization with smooth loss. Furthermore, the existing analysis requires the knowledge of optimal classifier for tuning the step size in order to achieve the desired bound. However, this information is usually not accessible in advanced. We also propose a strategy to address the limitation.
[ "['Rong Jin']", "Rong Jin" ]
cs.LG cs.AI
10.1017/S026988891300043X
1312.0049
null
null
http://arxiv.org/abs/1312.0049v1
2013-11-30T01:52:36Z
2013-11-30T01:52:36Z
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.
[ "Shehroz S.Khan, Michael G.Madden", "['Shehroz S. Khan' 'Michael G. Madden']" ]
null
null
1312.0232
null
null
http://arxiv.org/pdf/1312.0232v4
2017-05-30T13:19:17Z
2013-12-01T15:16:25Z
Stochastic continuum armed bandit problem of few linear parameters in high dimensions
We consider a stochastic continuum armed bandit problem where the arms are indexed by the $ell_2$ ball $B_{d}(1+nu)$ of radius $1+nu$ in $mathbb{R}^d$. The reward functions $r :B_{d}(1+nu) rightarrow mathbb{R}$ are considered to intrinsically depend on $k ll d$ unknown linear parameters so that $r(mathbf{x}) = g(mathbf{A} mathbf{x})$ where $mathbf{A}$ is a full rank $k times d$ matrix. Assuming the mean reward function to be smooth we make use of results from low-rank matrix recovery literature and derive an efficient randomized algorithm which achieves a regret bound of $O(C(k,d) n^{frac{1+k}{2+k}} (log n)^{frac{1}{2+k}})$ with high probability. Here $C(k,d)$ is at most polynomial in $d$ and $k$ and $n$ is the number of rounds or the sampling budget which is assumed to be known beforehand.
[ "['Hemant Tyagi' 'Sebastian Stich' 'Bernd Gärtner']" ]
cs.LG stat.ML
null
1312.0286
null
null
http://arxiv.org/pdf/1312.0286v2
2014-07-20T20:16:44Z
2013-12-01T23:17:06Z
Efficient Learning and Planning with Compressed Predictive States
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do not require a predetermined latent state structure as an input, they offer an attractive framework for model-based reinforcement learning when agents must plan without a priori access to a system model. Unfortunately, the expressiveness of PSRs comes with significant computational cost, and this cost is a major factor inhibiting the use of PSRs in applications. In order to alleviate this shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR learning approach combines recent advancements in dimensionality reduction, incremental matrix decomposition, and compressed sensing. We show how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization. Going further, we propose a planning framework which exploits these learned models. And we show that this approach facilitates model-learning and planning in large complex partially observable domains, a task that is infeasible without the principled use of compression.
[ "William L. Hamilton, Mahdi Milani Fard, and Joelle Pineau", "['William L. Hamilton' 'Mahdi Milani Fard' 'Joelle Pineau']" ]
cs.LG
null
1312.0412
null
null
http://arxiv.org/pdf/1312.0412v1
2013-12-02T10:58:01Z
2013-12-02T10:58:01Z
Practical Collapsed Stochastic Variational Inference for the HDP
Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance.
[ "['Arnim Bleier']", "Arnim Bleier" ]
math.PR cs.LG stat.ML
null
1312.0451
null
null
http://arxiv.org/pdf/1312.0451v5
2014-01-21T08:24:07Z
2013-12-02T13:41:44Z
Consistency of weighted majority votes
We revisit the classical decision-theoretic problem of weighted expert voting from a statistical learning perspective. In particular, we examine the consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush weighted majority and related rules. In the case of known expert competence levels, we give sharp error estimates for the optimal rule. When the competence levels are unknown, they must be empirically estimated. We provide frequentist and Bayesian analyses for this situation. Some of our proof techniques are non-standard and may be of independent interest. The bounds we derive are nearly optimal, and several challenging open problems are posed. Experimental results are provided to illustrate the theory.
[ "['Daniel Berend' 'Aryeh Kontorovich']", "Daniel Berend and Aryeh Kontorovich" ]
cs.LG cs.CL stat.ML
null
1312.0493
null
null
http://arxiv.org/pdf/1312.0493v1
2013-12-02T15:54:40Z
2013-12-02T15:54:40Z
Bidirectional Recursive Neural Networks for Token-Level Labeling with Structure
Recently, deep architectures, such as recurrent and recursive neural networks have been successfully applied to various natural language processing tasks. Inspired by bidirectional recurrent neural networks which use representations that summarize the past and future around an instance, we propose a novel architecture that aims to capture the structural information around an input, and use it to label instances. We apply our method to the task of opinion expression extraction, where we employ the binary parse tree of a sentence as the structure, and word vector representations as the initial representation of a single token. We conduct preliminary experiments to investigate its performance and compare it to the sequential approach.
[ "Ozan \\.Irsoy, Claire Cardie", "['Ozan İrsoy' 'Claire Cardie']" ]
cs.LG
null
1312.0512
null
null
http://arxiv.org/pdf/1312.0512v2
2014-03-13T12:02:10Z
2013-12-02T16:47:10Z
Sensing-Aware Kernel SVM
We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available. We show that the Bayes-optimum decision boundary is a hyperplane under a mapping defined by the likelihood function. Combining this with the maximum margin principle yields kernels for SVMs that leverage knowledge of the sensing model in an optimal way. We derive the optimum kernel for the bag-of-words (BoWs) sensing model and demonstrate its superior performance over other kernels in document and image classification tasks. These results indicate that such optimum sensing-aware kernel SVMs can match the performance of rather sophisticated state-of-the-art approaches.
[ "Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl", "['Weicong Ding' 'Prakash Ishwar' 'Venkatesh Saligrama' 'W. Clem Karl']" ]
cs.LG cs.SY stat.AP stat.ML
null
1312.0516
null
null
http://arxiv.org/pdf/1312.0516v2
2014-02-14T00:35:43Z
2013-12-02T16:58:10Z
Grid Topology Identification using Electricity Prices
The potential of recovering the topology of a grid using solely publicly available market data is explored here. In contemporary whole-sale electricity markets, real-time prices are typically determined by solving the network-constrained economic dispatch problem. Under a linear DC model, locational marginal prices (LMPs) correspond to the Lagrange multipliers of the linear program involved. The interesting observation here is that the matrix of spatiotemporally varying LMPs exhibits the following property: Once premultiplied by the weighted grid Laplacian, it yields a low-rank and sparse matrix. Leveraging this rich structure, a regularized maximum likelihood estimator (MLE) is developed to recover the grid Laplacian from the LMPs. The convex optimization problem formulated includes low rank- and sparsity-promoting regularizers, and it is solved using a scalable algorithm. Numerical tests on prices generated for the IEEE 14-bus benchmark provide encouraging topology recovery results.
[ "['Vassilis Kekatos' 'Georgios B. Giannakis' 'Ross Baldick']", "Vassilis Kekatos, Georgios B. Giannakis, Ross Baldick" ]
cs.LG
null
1312.0579
null
null
http://arxiv.org/pdf/1312.0579v1
2013-12-02T20:26:41Z
2013-12-02T20:26:41Z
SpeedMachines: Anytime Structured Prediction
Structured prediction plays a central role in machine learning applications from computational biology to computer vision. These models require significantly more computation than unstructured models, and, in many applications, algorithms may need to make predictions within a computational budget or in an anytime fashion. In this work we propose an anytime technique for learning structured prediction that, at training time, incorporates both structural elements and feature computation trade-offs that affect test-time inference. We apply our technique to the challenging problem of scene understanding in computer vision and demonstrate efficient and anytime predictions that gradually improve towards state-of-the-art classification performance as the allotted time increases.
[ "Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert", "['Alexander Grubb' 'Daniel Munoz' 'J. Andrew Bagnell' 'Martial Hebert']" ]
cs.LG stat.ML
null
1312.0624
null
null
http://arxiv.org/pdf/1312.0624v2
2013-12-13T18:47:20Z
2013-12-02T21:09:40Z
Efficient coordinate-descent for orthogonal matrices through Givens rotations
Optimizing over the set of orthogonal matrices is a central component in problems like sparse-PCA or tensor decomposition. Unfortunately, such optimization is hard since simple operations on orthogonal matrices easily break orthogonality, and correcting orthogonality usually costs a large amount of computation. Here we propose a framework for optimizing orthogonal matrices, that is the parallel of coordinate-descent in Euclidean spaces. It is based on {\em Givens-rotations}, a fast-to-compute operation that affects a small number of entries in the learned matrix, and preserves orthogonality. We show two applications of this approach: an algorithm for tensor decomposition that is used in learning mixture models, and an algorithm for sparse-PCA. We study the parameter regime where a Givens rotation approach converges faster and achieves a superior model on a genome-wide brain-wide mRNA expression dataset.
[ "Uri Shalit and Gal Chechik", "['Uri Shalit' 'Gal Chechik']" ]
cs.LG
null
1312.0786
null
null
http://arxiv.org/pdf/1312.0786v2
2014-02-19T11:13:57Z
2013-12-03T11:59:57Z
Image Representation Learning Using Graph Regularized Auto-Encoders
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning. In those learning tasks, the raw image vectors may not provide enough representation for their intrinsic structures due to their highly dense feature space. To overcome this problem, the raw image vectors should be mapped to a proper representation space which can capture the latent structure of the original data and represent the data explicitly for further learning tasks such as clustering. Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally invariant ideal to our image representation with auto-encoders and propose a novel method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. Extensive experiments on image clustering show encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-word cases.
[ "['Yiyi Liao' 'Yue Wang' 'Yong Liu']", "Yiyi Liao, Yue Wang, Yong Liu" ]
cs.AI cs.LG stat.ML
null
1312.0790
null
null
http://arxiv.org/pdf/1312.0790v2
2014-03-14T16:36:36Z
2013-12-03T12:12:23Z
Test Set Selection using Active Information Acquisition for Predictive Models
In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number of queries is limited by an overall budget. Arising in the context of two rather disparate applications- banking and medical diagnosis, we pose the active information acquisition problem as a constrained optimization problem. We propose two greedy iterative algorithms for solving the above problem. We conduct experiments with synthetic data and compare results of our proposed algorithms with few other baseline approaches. The experimental results show that our proposed approaches perform better than the baseline schemes.
[ "Sneha Chaudhari, Pankaj Dayama, Vinayaka Pandit, Indrajit Bhattacharya", "['Sneha Chaudhari' 'Pankaj Dayama' 'Vinayaka Pandit'\n 'Indrajit Bhattacharya']" ]
cs.LG cs.DS stat.ML
null
1312.0925
null
null
http://arxiv.org/pdf/1312.0925v3
2014-05-14T19:54:58Z
2013-12-03T20:37:28Z
Understanding Alternating Minimization for Matrix Completion
Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by and are still poorly understood. This is in part because the heuristic is iterative and non-convex in nature. We give a new algorithm based on Alternating Minimization that provably recovers an unknown low-rank matrix from a random subsample of its entries under a standard incoherence assumption. Our results reduce the sample size requirements of the Alternating Minimization approach by at least a quartic factor in the rank and the condition number of the unknown matrix. These improvements apply even if the matrix is only close to low-rank in the Frobenius norm. Our algorithm runs in nearly linear time in the dimension of the matrix and, in a broad range of parameters, gives the strongest sample bounds among all subquadratic time algorithms that we are aware of. Underlying our work is a new robust convergence analysis of the well-known Power Method for computing the dominant singular vectors of a matrix. This viewpoint leads to a conceptually simple understanding of Alternating Minimization. In addition, we contribute a new technique for controlling the coherence of intermediate solutions arising in iterative algorithms based on a smoothed analysis of the QR factorization. These techniques may be of interest beyond their application here.
[ "['Moritz Hardt']", "Moritz Hardt" ]
cs.DC cs.LG
null
1312.1031
null
null
http://arxiv.org/pdf/1312.1031v2
2014-03-23T22:13:17Z
2013-12-04T05:48:30Z
Analysis of Distributed Stochastic Dual Coordinate Ascent
In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates. However, no serious analysis has been provided to understand the updates and therefore the convergence rates. In the paper, we bridge the gap by providing a theoretical analysis of the convergence rates of the practical DisDCA algorithm. Our analysis helped by empirical studies has shown that it could yield an exponential speed-up in the convergence by increasing the number of dual updates at each iteration. This result justifies the superior performances of the practical DisDCA as compared to the naive variant. As a byproduct, our analysis also reveals the convergence behavior of the one-communication DisDCA.
[ "Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin", "['Tianbao Yang' 'Shenghuo Zhu' 'Rong Jin' 'Yuanqing Lin']" ]
cs.DS cs.LG math.PR math.ST stat.TH
null
1312.1054
null
null
http://arxiv.org/pdf/1312.1054v3
2014-05-19T13:26:05Z
2013-12-04T08:31:58Z
Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians
We provide an algorithm for properly learning mixtures of two single-dimensional Gaussians without any separability assumptions. Given $\tilde{O}(1/\varepsilon^2)$ samples from an unknown mixture, our algorithm outputs a mixture that is $\varepsilon$-close in total variation distance, in time $\tilde{O}(1/\varepsilon^5)$. Our sample complexity is optimal up to logarithmic factors, and significantly improves upon both Kalai et al., whose algorithm has a prohibitive dependence on $1/\varepsilon$, and Feldman et al., whose algorithm requires bounds on the mixture parameters and depends pseudo-polynomially in these parameters. One of our main contributions is an improved and generalized algorithm for selecting a good candidate distribution from among competing hypotheses. Namely, given a collection of $N$ hypotheses containing at least one candidate that is $\varepsilon$-close to an unknown distribution, our algorithm outputs a candidate which is $O(\varepsilon)$-close to the distribution. The algorithm requires ${O}(\log{N}/\varepsilon^2)$ samples from the unknown distribution and ${O}(N \log N/\varepsilon^2)$ time, which improves previous such results (such as the Scheff\'e estimator) from a quadratic dependence of the running time on $N$ to quasilinear. Given the wide use of such results for the purpose of hypothesis selection, our improved algorithm implies immediate improvements to any such use.
[ "Constantinos Daskalakis, Gautam Kamath", "['Constantinos Daskalakis' 'Gautam Kamath']" ]
stat.ML cs.LG
null
1312.1099
null
null
http://arxiv.org/pdf/1312.1099v1
2013-12-04T10:44:01Z
2013-12-04T10:44:01Z
Multiscale Dictionary Learning for Estimating Conditional Distributions
Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dictionary learning model, which expresses the conditional response density as a convex combination of dictionary densities, with the densities used and their weights dependent on the path through a tree decomposition of the feature space. A fast graph partitioning algorithm is applied to obtain the tree decomposition, with Bayesian methods then used to adaptively prune and average over different sub-trees in a soft probabilistic manner. The algorithm scales efficiently to approximately one million features. State of the art predictive performance is demonstrated for toy examples and two neuroscience applications including up to a million features.
[ "['Francesca Petralia' 'Joshua Vogelstein' 'David B. Dunson']", "Francesca Petralia, Joshua Vogelstein and David B. Dunson" ]
cs.LG
null
1312.1121
null
null
http://arxiv.org/pdf/1312.1121v1
2013-12-04T11:57:53Z
2013-12-04T11:57:53Z
Interpreting random forest classification models using a feature contribution method
Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For "black box" models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. By analysing feature contributions for a training dataset, the most significant variables can be determined and their typical contribution towards predictions made for individual classes, i.e., class-specific feature contribution "patterns", are discovered. These patterns represent a standard behaviour of the model and allow for an additional assessment of the model reliability for a new data. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.
[ "['Anna Palczewska' 'Jan Palczewski' 'Richard Marchese Robinson'\n 'Daniel Neagu']", "Anna Palczewska and Jan Palczewski and Richard Marchese Robinson and\n Daniel Neagu" ]
cs.DS cs.LG
null
1312.1277
null
null
http://arxiv.org/pdf/1312.1277v4
2019-04-15T14:49:36Z
2013-12-04T18:48:00Z
Bandits and Experts in Metric Spaces
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a solution for the multi-armed bandit problem in this setting. That is, for every metric space we define an isometry invariant which bounds from below the performance of Lipschitz MAB algorithms for this metric space, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions. We also address the full-feedback ("best expert") version of the problem, where after every round the payoffs from all arms are revealed.
[ "Robert Kleinberg, Aleksandrs Slivkins and Eli Upfal", "['Robert Kleinberg' 'Aleksandrs Slivkins' 'Eli Upfal']" ]
cs.LG
null
1312.1530
null
null
http://arxiv.org/pdf/1312.1530v2
2014-07-06T12:47:36Z
2013-12-05T13:00:23Z
Bandit Online Optimization Over the Permutahedron
The permutahedron is the convex polytope with vertex set consisting of the vectors $(\pi(1),\dots, \pi(n))$ for all permutations (bijections) $\pi$ over $\{1,\dots, n\}$. We study a bandit game in which, at each step $t$, an adversary chooses a hidden weight weight vector $s_t$, a player chooses a vertex $\pi_t$ of the permutahedron and suffers an observed loss of $\sum_{i=1}^n \pi(i) s_t(i)$. A previous algorithm CombBand of Cesa-Bianchi et al (2009) guarantees a regret of $O(n\sqrt{T \log n})$ for a time horizon of $T$. Unfortunately, CombBand requires at each step an $n$-by-$n$ matrix permanent approximation to within improved accuracy as $T$ grows, resulting in a total running time that is super linear in $T$, making it impractical for large time horizons. We provide an algorithm of regret $O(n^{3/2}\sqrt{T})$ with total time complexity $O(n^3T)$. The ideas are a combination of CombBand and a recent algorithm by Ailon (2013) for online optimization over the permutahedron in the full information setting. The technical core is a bound on the variance of the Plackett-Luce noisy sorting process's "pseudo loss". The bound is obtained by establishing positive semi-definiteness of a family of 3-by-3 matrices generated from rational functions of exponentials of 3 parameters.
[ "['Nir Ailon' 'Kohei Hatano' 'Eiji Takimoto']", "Nir Ailon and Kohei Hatano and Eiji Takimoto" ]
stat.ML cs.LG cs.NA
null
1312.1613
null
null
http://arxiv.org/pdf/1312.1613v1
2013-12-05T16:49:05Z
2013-12-05T16:49:05Z
Max-Min Distance Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been a popular representation method for pattern classification problem. It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basic matrix and a nonnegative coefficient matrix, and the coefficient matrix is used as the new representation. However, traditional NMF methods ignore the class labels of the data samples. In this paper, we proposed a supervised novel NMF algorithm to improve the discriminative ability of the new representation. Using the class labels, we separate all the data sample pairs into within-class pairs and between-class pairs. To improve the discriminate ability of the new NMF representations, we hope that the maximum distance of the within-class pairs in the new NMF space could be minimized, while the minimum distance of the between-class pairs pairs could be maximized. With this criterion, we construct an objective function and optimize it with regard to basic and coefficient matrices and slack variables alternatively, resulting in a iterative algorithm.
[ "['Jim Jing-Yan Wang']", "Jim Jing-Yan Wang" ]
stat.ML cs.LG cs.NA math.NA math.OC
null
1312.1666
null
null
http://arxiv.org/pdf/1312.1666v2
2015-06-16T05:05:40Z
2013-12-05T20:04:52Z
Semi-Stochastic Gradient Descent Methods
In this paper we study the problem of minimizing the average of a large number ($n$) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, following a geometric law. The total work needed for the method to output an $\varepsilon$-accurate solution in expectation, measured in the number of passes over data, or equivalently, in units equivalent to the computation of a single gradient of the loss, is $O((\kappa/n)\log(1/\varepsilon))$, where $\kappa$ is the condition number. This is achieved by running the method for $O(\log(1/\varepsilon))$ epochs, with a single gradient evaluation and $O(\kappa)$ stochastic gradient evaluations in each. The SVRG method of Johnson and Zhang arises as a special case. If our method is limited to a single epoch only, it needs to evaluate at most $O((\kappa/\varepsilon)\log(1/\varepsilon))$ stochastic gradients. In contrast, SVRG requires $O(\kappa/\varepsilon^2)$ stochastic gradients. To illustrate our theoretical results, S2GD only needs the workload equivalent to about 2.1 full gradient evaluations to find an $10^{-6}$-accurate solution for a problem with $n=10^9$ and $\kappa=10^3$.
[ "['Jakub Konečný' 'Peter Richtárik']", "Jakub Kone\\v{c}n\\'y and Peter Richt\\'arik" ]
cs.LG
null
1312.1737
null
null
http://arxiv.org/pdf/1312.1737v1
2013-12-05T23:53:45Z
2013-12-05T23:53:45Z
Curriculum Learning for Handwritten Text Line Recognition
Recurrent Neural Networks (RNN) have recently achieved the best performance in off-line Handwriting Text Recognition. At the same time, learning RNN by gradient descent leads to slow convergence, and training times are particularly long when the training database consists of full lines of text. In this paper, we propose an easy way to accelerate stochastic gradient descent in this set-up, and in the general context of learning to recognize sequences. The principle is called Curriculum Learning, or shaping. The idea is to first learn to recognize short sequences before training on all available training sequences. Experiments on three different handwritten text databases (Rimes, IAM, OpenHaRT) show that a simple implementation of this strategy can significantly speed up the training of RNN for Text Recognition, and even significantly improve performance in some cases.
[ "J\\'er\\^ome Louradour and Christopher Kermorvant", "['Jérôme Louradour' 'Christopher Kermorvant']" ]
cs.LG cs.CV
null
1312.1743
null
null
http://arxiv.org/pdf/1312.1743v2
2014-06-13T04:10:06Z
2013-12-06T00:55:51Z
Dual coordinate solvers for large-scale structural SVMs
This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, out-of-core training datasets. Current strategies for large-scale learning fall into one of two camps; batch algorithms which solve the learning problem given a finite datasets, and online algorithms which can process out-of-core datasets. The former typically requires datasets small enough to fit in memory. The latter is often phrased as a stochastic optimization problem; such algorithms enjoy strong theoretical properties but often require manual tuned annealing schedules, and may converge slowly for problems with large output spaces (e.g., structural SVMs). We discuss an algorithm for an "intermediate" regime in which the data is too large to fit in memory, but the active constraints (support vectors) are small enough to remain in memory. In this case, one can design rather efficient learning algorithms that are as stable as batch algorithms, but capable of processing out-of-core datasets. We have developed such a MATLAB-based solver and used it to train a collection of recognition systems for articulated pose estimation, facial analysis, 3D object recognition, and action classification, all with publicly-available code. This writeup describes the solver in detail.
[ "['Deva Ramanan']", "Deva Ramanan" ]
cs.LG
null
1312.1847
null
null
http://arxiv.org/pdf/1312.1847v2
2014-02-19T17:55:37Z
2013-12-06T12:55:05Z
Understanding Deep Architectures using a Recursive Convolutional Network
A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kernel sizes, etc. Complicating this further is the fact that each of these influence not only the numbers and dimensions of the activation units, but also the total number of parameters. In this paper we focus on assessing the independent contributions of three of these linked variables: The numbers of layers, feature maps, and parameters. To accomplish this, we employ a recursive convolutional network whose weights are tied between layers; this allows us to vary each of the three factors in a controlled setting. We find that while increasing the numbers of layers and parameters each have clear benefit, the number of feature maps (and hence dimensionality of the representation) appears ancillary, and finds most of its benefit through the introduction of more weights. Our results (i) empirically confirm the notion that adding layers alone increases computational power, within the context of convolutional layers, and (ii) suggest that precise sizing of convolutional feature map dimensions is itself of little concern; more attention should be paid to the number of parameters in these layers instead.
[ "David Eigen, Jason Rolfe, Rob Fergus, Yann LeCun", "['David Eigen' 'Jason Rolfe' 'Rob Fergus' 'Yann LeCun']" ]
cs.NE cs.CV cs.LG stat.ML
null
1312.1909
null
null
http://arxiv.org/pdf/1312.1909v1
2013-11-18T17:56:11Z
2013-11-18T17:56:11Z
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.
[ "['Qi Wang' 'Joseph JaJa']", "Qi Wang and Joseph JaJa" ]
cs.SY cs.LG stat.ML
null
1312.2132
null
null
http://arxiv.org/pdf/1312.2132v1
2013-12-07T19:19:03Z
2013-12-07T19:19:03Z
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.
[ "Dorsa Sadigh, Henrik Ohlsson, S. Shankar Sastry, Sanjit A. Seshia", "['Dorsa Sadigh' 'Henrik Ohlsson' 'S. Shankar Sastry' 'Sanjit A. Seshia']" ]
cs.LG cs.CL cs.NE
null
1312.2137
null
null
http://arxiv.org/pdf/1312.2137v1
2013-12-07T19:55:02Z
2013-12-07T19:55:02Z
End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks
Most phoneme recognition state-of-the-art systems rely on a classical neural network classifiers, fed with highly tuned features, such as MFCC or PLP features. Recent advances in ``deep learning'' approaches questioned such systems, but while some attempts were made with simpler features such as spectrograms, state-of-the-art systems still rely on MFCCs. This might be viewed as a kind of failure from deep learning approaches, which are often claimed to have the ability to train with raw signals, alleviating the need of hand-crafted features. In this paper, we investigate a convolutional neural network approach for raw speech signals. While convolutional architectures got tremendous success in computer vision or text processing, they seem to have been let down in the past recent years in the speech processing field. We show that it is possible to learn an end-to-end phoneme sequence classifier system directly from raw signal, with similar performance on the TIMIT and WSJ datasets than existing systems based on MFCC, questioning the need of complex hand-crafted features on large datasets.
[ "Dimitri Palaz, Ronan Collobert, Mathew Magimai.-Doss", "['Dimitri Palaz' 'Ronan Collobert' 'Mathew Magimai. -Doss']" ]
cs.SI cs.LG stat.ML
null
1312.2154
null
null
http://arxiv.org/pdf/1312.2154v1
2013-12-07T23:42:55Z
2013-12-07T23:42:55Z
Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks
Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.
[ "['Tomoki Kobayashi' 'Koji Eguchi']", "Tomoki Kobayashi, Koji Eguchi" ]
cs.LG cs.SI stat.ML
null
1312.2164
null
null
http://arxiv.org/pdf/1312.2164v2
2014-04-16T03:53:04Z
2013-12-08T01:58:39Z
Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more constraints than a single product, endless user attentions, unlimited budget and unbounded time; in reality, multiple products need to be advertised, each user can tolerate only a small number of recommendations, influencing user has a cost and advertisers have only limited budgets, and the adoptions need to be maximized within a short time window. Given theses myriads of user, monetary, and timing constraints, it is extremely challenging for the host to design principled and efficient viral market algorithms with provable guarantees. In this paper, we provide a novel solution by formulating the problem as a submodular maximization in a continuous-time diffusion model under an intersection of a matroid and multiple knapsack constraints. We also propose an adaptive threshold greedy algorithm which can be faster than the traditional greedy algorithm with lazy evaluation, and scalable to networks with million of nodes. Furthermore, our mathematical formulation allows us to prove that the algorithm can achieve an approximation factor of $k_a/(2+2 k)$ when $k_a$ out of the $k$ knapsack constraints are active, which also improves over previous guarantees from combinatorial optimization literature. In the case when influencing each user has uniform cost, the approximation becomes even better to a factor of $1/3$. Extensive synthetic and real world experiments demonstrate that our budgeted influence maximization algorithm achieves the-state-of-the-art in terms of both effectiveness and scalability, often beating the next best by significant margins.
[ "Nan Du, Yingyu Liang, Maria Florina Balcan, Le Song", "['Nan Du' 'Yingyu Liang' 'Maria Florina Balcan' 'Le Song']" ]
stat.ML cs.LG
null
1312.2171
null
null
http://arxiv.org/pdf/1312.2171v3
2014-11-24T19:21:22Z
2013-12-08T03:40:47Z
bartMachine: Machine Learning with Bayesian Additive Regression Trees
We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.
[ "['Adam Kapelner' 'Justin Bleich']", "Adam Kapelner and Justin Bleich" ]
cs.CR cs.LG cs.NI
null
1312.2177
null
null
http://arxiv.org/pdf/1312.2177v2
2015-05-09T06:07:35Z
2013-12-08T06:56:21Z
Machine Learning Techniques for Intrusion Detection
An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used in today's IDS are not able to deal with the dynamic and complex nature of cyber attacks on computer networks. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. In this paper, we study several such schemes and compare their performance. We divide the schemes into methods based on classical artificial intelligence (AI) and methods based on computational intelligence (CI). We explain how various characteristics of CI techniques can be used to build efficient IDS.
[ "['Mahdi Zamani' 'Mahnush Movahedi']", "Mahdi Zamani and Mahnush Movahedi" ]
cs.LG
null
1312.2451
null
null
http://arxiv.org/pdf/1312.2451v1
2013-12-06T18:25:15Z
2013-12-06T18:25:15Z
CEAI: CCM based Email Authorship Identification Model
In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2].
[ "Sarwat Nizamani, Nasrullah Memon", "['Sarwat Nizamani' 'Nasrullah Memon']" ]
cs.CG cs.LG math.DS nlin.CD physics.data-an
null
1312.2482
null
null
http://arxiv.org/pdf/1312.2482v2
2014-03-24T14:33:37Z
2013-12-09T16:02:23Z
Automatic recognition and tagging of topologically different regimes in dynamical systems
Complex systems are commonly modeled using nonlinear dynamical systems. These models are often high-dimensional and chaotic. An important goal in studying physical systems through the lens of mathematical models is to determine when the system undergoes changes in qualitative behavior. A detailed description of the dynamics can be difficult or impossible to obtain for high-dimensional and chaotic systems. Therefore, a more sensible goal is to recognize and mark transitions of a system between qualitatively different regimes of behavior. In practice, one is interested in developing techniques for detection of such transitions from sparse observations, possibly contaminated by noise. In this paper we develop a framework to accurately tag different regimes of complex systems based on topological features. In particular, our framework works with a high degree of success in picking out a cyclically orbiting regime from a stationary equilibrium regime in high-dimensional stochastic dynamical systems.
[ "Jesse Berwald, Marian Gidea and Mikael Vejdemo-Johansson", "['Jesse Berwald' 'Marian Gidea' 'Mikael Vejdemo-Johansson']" ]
cs.LG
10.1109/IJCNN.2013.6706862
1312.2578
null
null
http://arxiv.org/abs/1312.2578v2
2014-04-28T20:08:47Z
2013-12-09T20:58:16Z
Kernel-based Distance Metric Learning in the Output Space
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2- or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches.
[ "Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos", "['Cong Li' 'Michael Georgiopoulos' 'Georgios C. Anagnostopoulos']" ]
cs.LG
null
1312.2606
null
null
http://arxiv.org/pdf/1312.2606v1
2013-12-09T21:27:23Z
2013-12-09T21:27:23Z
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification. It extends similar hypothesis spaces that have previously considered in the literature. Assuming this space, an improved Empirical Rademacher Complexity-based generalization bound is derived. The analysis is itself extended to an MKL setting. The connection between the proposed hypothesis space and a Group-Lasso type regularizer is discussed. Finally, experimental results, with some SVM-based Multi-Task Learning problems, underline the quality of the derived bounds and validate the paper's analysis.
[ "Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos", "['Cong Li' 'Michael Georgiopoulos' 'Georgios C. Anagnostopoulos']" ]
cs.LG cs.AI
null
1312.2710
null
null
http://arxiv.org/pdf/1312.2710v1
2013-12-10T08:11:14Z
2013-12-10T08:11:14Z
Improving circuit miniaturization and its efficiency using Rough Set Theory
High-speed, accuracy, meticulousness and quick response are notion of the vital necessities for modern digital world. An efficient electronic circuit unswervingly affects the maneuver of the whole system. Different tools are required to unravel different types of engineering tribulations. Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem. So the need of circuit miniaturization is always there. It saves a lot of time and power that is wasted in switching of gates, the wiring-crises is reduced, cross-sectional area of chip is reduced, the number of transistors that can implemented in chip is multiplied many folds. Therefore to trounce with this problem we have proposed an Artificial intelligence (AI) based approach that make use of Rough Set Theory for its implementation. Theory of rough set has been proposed by Z Pawlak in the year 1982. Rough set theory is a new mathematical tool which deals with uncertainty and vagueness. Decisions can be generated using rough set theory by reducing the unwanted and superfluous data. We have condensed the number of gates without upsetting the productivity of the given circuit. This paper proposes an approach with the help of rough set theory which basically lessens the number of gates in the circuit, based on decision rules.
[ "['Sarvesh SS Rawat' 'Dheeraj Dilip Mor' 'Anugrah Kumar'\n 'Sanjiban Shekar Roy' 'Rohit kumar']", "Sarvesh SS Rawat, Dheeraj Dilip Mor, Anugrah Kumar, Sanjiban Shekar\n Roy, Rohit kumar" ]
cs.LG
10.5121/ijcsity.2013.1408
1312.2789
null
null
http://arxiv.org/abs/1312.2789v1
2013-12-10T13:16:02Z
2013-12-10T13:16:02Z
Performance Analysis Of Regularized Linear Regression Models For Oxazolines And Oxazoles Derivitive Descriptor Dataset
Regularized regression techniques for linear regression have been created the last few ten years to reduce the flaws of ordinary least squares regression with regard to prediction accuracy. In this paper, new methods for using regularized regression in model choice are introduced, and we distinguish the conditions in which regularized regression develops our ability to discriminate models. We applied all the five methods that use penalty-based (regularization) shrinkage to handle Oxazolines and Oxazoles derivatives descriptor dataset with far more predictors than observations. The lasso, ridge, elasticnet, lars and relaxed lasso further possess the desirable property that they simultaneously select relevant predictive descriptors and optimally estimate their effects. Here, we comparatively evaluate the performance of five regularized linear regression methods The assessment of the performance of each model by means of benchmark experiments is an established exercise. Cross-validation and resampling methods are generally used to arrive point evaluates the efficiencies which are compared to recognize methods with acceptable features. Predictive accuracy was evaluated using the root mean squared error (RMSE) and Square of usual correlation between predictors and observed mean inhibitory concentration of antitubercular activity (R square). We found that all five regularized regression models were able to produce feasible models and efficient capturing the linearity in the data. The elastic net and lars had similar accuracies as well as lasso and relaxed lasso had similar accuracies but outperformed ridge regression in terms of the RMSE and R square metrics.
[ "Doreswamy and Chanabasayya .M. Vastrad", "['Doreswamy' 'Chanabasayya . M. Vastrad']" ]
cs.LG
null
1312.2936
null
null
http://arxiv.org/pdf/1312.2936v1
2013-12-10T20:36:04Z
2013-12-10T20:36:04Z
Active Player Modelling
We argue for the use of active learning methods for player modelling. In active learning, the learning algorithm chooses where to sample the search space so as to optimise learning progress. We hypothesise that player modelling based on active learning could result in vastly more efficient learning, but will require big changes in how data is collected. Some example active player modelling scenarios are described. A particular form of active learning is also equivalent to an influential formalisation of (human and machine) curiosity, and games with active learning could therefore be seen as being curious about the player. We further hypothesise that this form of curiosity is symmetric, and therefore that games that explore their players based on the principles of active learning will turn out to select game configurations that are interesting to the player that is being explored.
[ "Julian Togelius, Noor Shaker, Georgios N. Yannakakis", "['Julian Togelius' 'Noor Shaker' 'Georgios N. Yannakakis']" ]
q-bio.QM cs.LG math.OC q-bio.BM stat.ML
null
1312.2988
null
null
http://arxiv.org/pdf/1312.2988v5
2015-04-08T14:21:09Z
2013-12-10T22:45:06Z
Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, respectively. This paper presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning. Different from existing single-family EC analysis that uses residue co-evolution information in only the target protein family, our joint EC analysis uses residue co-evolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement joint EC analysis, we model a set of related protein families using Gaussian graphical models (GGM) and then co-estimate their precision matrices by maximum-likelihood, subject to the constraint that the precision matrices shall share similar residue co-evolution patterns. To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework. Experiments show that our method can predict contacts much more accurately than existing methods, and that our method performs better on both conserved and family-specific contacts.
[ "['Jianzhu Ma' 'Sheng Wang' 'Zhiyong Wang' 'Jinbo Xu']", "Jianzhu Ma, Sheng Wang, Zhiyong Wang and Jinbo Xu" ]
stat.ML cs.LG
null
1312.3386
null
null
http://arxiv.org/pdf/1312.3386v2
2013-12-25T13:10:44Z
2013-12-12T02:37:36Z
Clustering for high-dimension, low-sample size data using distance vectors
In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this fact, we propose an efficient and simple clustering approach, called distance vector clustering, for HDLSS data. Under the assumptions given in the work of Hall et al. (2005), we show the proposed approach provides a true cluster label under milder conditions when the dimension tends to infinity with the sample size fixed. The effectiveness of the distance vector clustering approach is illustrated through a numerical experiment and real data analysis.
[ "['Yoshikazu Terada']", "Yoshikazu Terada" ]
cs.LG
null
1312.3388
null
null
http://arxiv.org/pdf/1312.3388v1
2013-12-12T02:46:07Z
2013-12-12T02:46:07Z
Online Bayesian Passive-Aggressive Learning
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin model could limit its capability in discovering descriptive structures underlying complex data. This pa- per presents online Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA and extends naturally to incorporate latent variables and perform nonparametric Bayesian inference, thus providing great flexibility for explorative analysis. We apply BayesPA to topic modeling and derive efficient online learning algorithms for max-margin topic models. We further develop nonparametric methods to resolve the number of topics. Experimental results on real datasets show that our approaches significantly improve time efficiency while maintaining comparable results with the batch counterparts.
[ "Tianlin Shi and Jun Zhu", "['Tianlin Shi' 'Jun Zhu']" ]
cs.LG
null
1312.3393
null
null
http://arxiv.org/pdf/1312.3393v2
2013-12-17T10:30:42Z
2013-12-12T03:08:46Z
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.
[ "['Masrour Zoghi' 'Shimon Whiteson' 'Remi Munos' 'Maarten de Rijke']", "Masrour Zoghi, Shimon Whiteson, Remi Munos, Maarten de Rijke" ]
cs.CV cs.LG stat.ML
null
1312.3429
null
null
http://arxiv.org/pdf/1312.3429v2
2013-12-16T16:11:52Z
2013-12-12T10:03:47Z
Unsupervised learning of depth and motion
We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames in a video, or the combination of both. We show that learning depth and motion cues, as well as their combinations, from data is possible within a single type of architecture and a single type of learning algorithm, by using biologically inspired "complex cell" like units, which encode correlations between the pixels across image pairs. Our experimental results show that the learning of depth and motion makes it possible to achieve state-of-the-art performance in 3-D activity analysis, and to outperform existing hand-engineered 3-D motion features by a very large margin.
[ "Kishore Konda, Roland Memisevic", "['Kishore Konda' 'Roland Memisevic']" ]
cs.LG cs.CV stat.ML
null
1312.3522
null
null
http://arxiv.org/pdf/1312.3522v3
2014-10-12T22:10:13Z
2013-12-12T15:26:57Z
Sparse Matrix-based Random Projection for Classification
As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional data with high probability. Considering this technique is mainly exploited for the task of classification, this paper is developed to study the construction of random matrix from the viewpoint of feature selection, rather than of traditional distance preservation. This yields a somewhat surprising theoretical result, that is, the sparse random matrix with exactly one nonzero element per column, can present better feature selection performance than other more dense matrices, if the projection dimension is sufficiently large (namely, not much smaller than the number of feature elements); otherwise, it will perform comparably to others. For random projection, this theoretical result implies considerable improvement on both complexity and performance, which is widely confirmed with the classification experiments on both synthetic data and real data.
[ "['Weizhi Lu' 'Weiyu Li' 'Kidiyo Kpalma' 'Joseph Ronsin']", "Weizhi Lu and Weiyu Li and Kidiyo Kpalma and Joseph Ronsin" ]
cs.LG
10.1007/978-3-642-40728-4_17
1312.3811
null
null
http://arxiv.org/abs/1312.3811v1
2013-12-13T14:10:30Z
2013-12-13T14:10:30Z
Efficient Baseline-free Sampling in Parameter Exploring Policy Gradients: Super Symmetric PGPE
Policy Gradient methods that explore directly in parameter space are among the most effective and robust direct policy search methods and have drawn a lot of attention lately. The basic method from this field, Policy Gradients with Parameter-based Exploration, uses two samples that are symmetric around the current hypothesis to circumvent misleading reward in \emph{asymmetrical} reward distributed problems gathered with the usual baseline approach. The exploration parameters are still updated by a baseline approach - leaving the exploration prone to asymmetric reward distributions. In this paper we will show how the exploration parameters can be sampled quasi symmetric despite having limited instead of free parameters for exploration. We give a transformation approximation to get quasi symmetric samples with respect to the exploration without changing the overall sampling distribution. Finally we will demonstrate that sampling symmetrically also for the exploration parameters is superior in needs of samples and robustness than the original sampling approach.
[ "['Frank Sehnke']", "Frank Sehnke" ]
cs.AI cs.LG
null
1312.3903
null
null
http://arxiv.org/pdf/1312.3903v1
2013-12-13T18:32:51Z
2013-12-13T18:32:51Z
A Methodology for Player Modeling based on Machine Learning
AI is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, player modeling is becoming an important one. The main idea is to understand and model the player characteristics and behaviors in order to develop a better AI. In this work, we discuss several aspects of this new field. We proposed a taxonomy to organize the area, discussing several facets of this topic, ranging from implementation decisions up to what a model attempts to describe. We then classify, in our taxonomy, some of the most important works in this field. We also presented a generic approach to deal with player modeling using ML, and we instantiated this approach to model players' preferences in the game Civilization IV. The instantiation of this approach has several steps. We first discuss a generic representation, regardless of what is being modeled, and evaluate it performing experiments with the strategy game Civilization IV. Continuing the instantiation of the proposed approach we evaluated the applicability of using game score information to distinguish different preferences. We presented a characterization of virtual agents in the game, comparing their behavior with their stated preferences. Once we have characterized these agents, we were able to observe that different preferences generate different behaviors, measured by several game indicators. We then tackled the preference modeling problem as a binary classification task, with a supervised learning approach. We compared four different methods, based on different paradigms (SVM, AdaBoost, NaiveBayes and JRip), evaluating them on a set of matches played by different virtual agents. We conclude our work using the learned models to infer human players' preferences. Using some of the evaluated classifiers we obtained accuracies over 60% for most of the inferred preferences.
[ "Marlos C. Machado", "['Marlos C. Machado']" ]
cs.LG stat.ML
null
1312.3970
null
null
http://arxiv.org/pdf/1312.3970v1
2013-12-13T21:59:00Z
2013-12-13T21:59:00Z
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which learning algorithms to use for filtering as well as their effects on multiple learning algorithms over a large set of data sets has not been done. Previous work has generally been limited due to the large computational requirements to run such an experiment, and, thus, the examination has generally been limited to learning algorithms that are computationally inexpensive and using a small number of data sets. In this paper, we examine 9 learning algorithms as filtering algorithms as well as examining the effects of filtering in the 9 chosen learning algorithms on a set of 54 data sets. In addition to using each learning algorithm individually as a filter, we also use the set of learning algorithms as an ensemble filter and use an adaptive algorithm that selects a subset of the learning algorithms for filtering for a specific task and learning algorithm. We find that for most cases, using an ensemble of learning algorithms for filtering produces the greatest increase in classification accuracy. We also compare filtering with a majority voting ensemble. The voting ensemble significantly outperforms filtering unless there are high amounts of noise present in the data set. Additionally, we find that a majority voting ensemble is robust to noise as filtering with a voting ensemble does not increase the classification accuracy of the voting ensemble.
[ "['Michael R. Smith' 'Tony Martinez']", "Michael R. Smith and Tony Martinez" ]
cs.CV cs.LG
null
1312.3989
null
null
http://arxiv.org/pdf/1312.3989v1
2013-12-14T00:28:32Z
2013-12-14T00:28:32Z
Classifiers With a Reject Option for Early Time-Series Classification
Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the "agreement" of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives.
[ "Nima Hatami and Camelia Chira", "['Nima Hatami' 'Camelia Chira']" ]
cs.CV cs.LG
10.1109/ICCIS.2008.4670763
1312.3990
null
null
http://arxiv.org/abs/1312.3990v1
2013-12-14T00:29:36Z
2013-12-14T00:29:36Z
ECOC-Based Training of Neural Networks for Face Recognition
Error Correcting Output Codes, ECOC, is an output representation method capable of discovering some of the errors produced in classification tasks. This paper describes the application of ECOC to the training of feed forward neural networks, FFNN, for improving the overall accuracy of classification systems. Indeed, to improve the generalization of FFNN classifiers, this paper proposes an ECOC-Based training method for Neural Networks that use ECOC as the output representation, and adopts the traditional Back-Propagation algorithm, BP, to adjust weights of the network. Experimental results for face recognition problem on Yale database demonstrate the effectiveness of our method. With a rejection scheme defined by a simple robustness rate, high reliability is achieved in this application.
[ "Nima Hatami, Reza Ebrahimpour, Reza Ghaderi", "['Nima Hatami' 'Reza Ebrahimpour' 'Reza Ghaderi']" ]
cs.CL cs.LG
null
1312.4092
null
null
http://arxiv.org/pdf/1312.4092v1
2013-12-14T21:48:49Z
2013-12-14T21:48:49Z
Domain adaptation for sequence labeling using hidden Markov models
Most natural language processing systems based on machine learning are not robust to domain shift. For example, a state-of-the-art syntactic dependency parser trained on Wall Street Journal sentences has an absolute drop in performance of more than ten points when tested on textual data from the Web. An efficient solution to make these methods more robust to domain shift is to first learn a word representation using large amounts of unlabeled data from both domains, and then use this representation as features in a supervised learning algorithm. In this paper, we propose to use hidden Markov models to learn word representations for part-of-speech tagging. In particular, we study the influence of using data from the source, the target or both domains to learn the representation and the different ways to represent words using an HMM.
[ "Edouard Grave (LIENS, INRIA Paris - Rocquencourt), Guillaume Obozinski\n (LIGM), Francis Bach (LIENS, INRIA Paris - Rocquencourt)", "['Edouard Grave' 'Guillaume Obozinski' 'Francis Bach']" ]
cs.LG cs.DC
null
1312.4108
null
null
http://arxiv.org/pdf/1312.4108v1
2013-12-15T05:42:51Z
2013-12-15T05:42:51Z
A MapReduce based distributed SVM algorithm for binary classification
Although Support Vector Machine (SVM) algorithm has a high generalization property to classify for unseen examples after training phase and it has small loss value, the algorithm is not suitable for real-life classification and regression problems. SVMs cannot solve hundreds of thousands examples in training dataset. In previous studies on distributed machine learning algorithms, SVM is trained over a costly and preconfigured computer environment. In this research, we present a MapReduce based distributed parallel SVM training algorithm for binary classification problems. This work shows how to distribute optimization problem over cloud computing systems with MapReduce technique. In the second step of this work, we used statistical learning theory to find the predictive hypothesis that minimize our empirical risks from hypothesis spaces that created with reduce function of MapReduce. The results of this research are important for training of big datasets for SVM algorithm based classification problems. We provided that iterative training of split dataset with MapReduce technique; accuracy of the classifier function will converge to global optimal classifier function's accuracy in finite iteration size. The algorithm performance was measured on samples from letter recognition and pen-based recognition of handwritten digits dataset.
[ "Ferhat \\\"Ozg\\\"ur \\c{C}atak, Mehmet Erdal Balaban", "['Ferhat Özgür Çatak' 'Mehmet Erdal Balaban']" ]
cs.LG cs.DC
null
1312.4176
null
null
http://arxiv.org/pdf/1312.4176v3
2014-11-10T13:36:34Z
2013-12-15T18:08:27Z
Distributed k-means algorithm
In this paper we provide a fully distributed implementation of the k-means clustering algorithm, intended for wireless sensor networks where each agent is endowed with a possibly high-dimensional observation (e.g., position, humidity, temperature, etc.) The proposed algorithm, by means of one-hop communication, partitions the agents into measure-dependent groups that have small in-group and large out-group "distances". Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters.The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm. Some numerical examples illustrate the capabilities of the proposed solution.
[ "Gabriele Oliva, Roberto Setola, and Christoforos N. Hadjicostis", "['Gabriele Oliva' 'Roberto Setola' 'Christoforos N. Hadjicostis']" ]
cs.LG
null
1312.4209
null
null
http://arxiv.org/pdf/1312.4209v1
2013-12-15T23:40:49Z
2013-12-15T23:40:49Z
Feature Graph Architectures
In this article we propose feature graph architectures (FGA), which are deep learning systems employing a structured initialisation and training method based on a feature graph which facilitates improved generalisation performance compared with a standard shallow architecture. The goal is to explore alternative perspectives on the problem of deep network training. We evaluate FGA performance for deep SVMs on some experimental datasets, and show how generalisation and stability results may be derived for these models. We describe the effect of permutations on the model accuracy, and give a criterion for the optimal permutation in terms of feature correlations. The experimental results show that the algorithm produces robust and significant test set improvements over a standard shallow SVM training method for a range of datasets. These gains are achieved with a moderate increase in time complexity.
[ "['Richard Davis' 'Sanjay Chawla' 'Philip Leong']", "Richard Davis, Sanjay Chawla, Philip Leong" ]
cs.LG
null
1312.4314
null
null
http://arxiv.org/pdf/1312.4314v3
2014-03-09T20:15:03Z
2013-12-16T11:15:10Z
Learning Factored Representations in a Deep Mixture of Experts
Mixtures of Experts combine the outputs of several "expert" networks, each of which specializes in a different part of the input space. This is achieved by training a "gating" network that maps each input to a distribution over the experts. Such models show promise for building larger networks that are still cheap to compute at test time, and more parallelizable at training time. In this this work, we extend the Mixture of Experts to a stacked model, the Deep Mixture of Experts, with multiple sets of gating and experts. This exponentially increases the number of effective experts by associating each input with a combination of experts at each layer, yet maintains a modest model size. On a randomly translated version of the MNIST dataset, we find that the Deep Mixture of Experts automatically learns to develop location-dependent ("where") experts at the first layer, and class-specific ("what") experts at the second layer. In addition, we see that the different combinations are in use when the model is applied to a dataset of speech monophones. These demonstrate effective use of all expert combinations.
[ "['David Eigen' \"Marc'Aurelio Ranzato\" 'Ilya Sutskever']", "David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever" ]
cs.CV cs.LG cs.NE
null
1312.4384
null
null
http://arxiv.org/pdf/1312.4384v1
2013-12-16T14:51:00Z
2013-12-16T14:51:00Z
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images
We attack the problem of learning concepts automatically from noisy web image search results. Going beyond low level attributes, such as colour and texture, we explore weakly-labelled datasets for the learning of higher level concepts, such as scene categories. The idea is based on discovering common characteristics shared among subsets of images by posing a method that is able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Rectifying Self Organizing Maps (RSOM). Given an image collection returned for a concept query, RSOM provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. The proposed method outperforms the state-of-the-art studies on the task of learning low-level concepts, and it is competitive in learning higher level concepts as well. It is capable to work at large scale with no supervision through exploiting the available sources.
[ "['Eren Golge' 'Pinar Duygulu']", "Eren Golge and Pinar Duygulu" ]
cs.NE cs.CV cs.LG
null
1312.4400
null
null
http://arxiv.org/pdf/1312.4400v3
2014-03-04T05:15:42Z
2013-12-16T15:34:13Z
Network In Network
We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.
[ "['Min Lin' 'Qiang Chen' 'Shuicheng Yan']", "Min Lin, Qiang Chen, Shuicheng Yan" ]
cs.LG
null
1312.4405
null
null
http://arxiv.org/pdf/1312.4405v1
2013-12-16T15:40:05Z
2013-12-16T15:40:05Z
Learning Deep Representations By Distributed Random Samplings
In this paper, we propose an extremely simple deep model for the unsupervised nonlinear dimensionality reduction -- deep distributed random samplings, which performs like a stack of unsupervised bootstrap aggregating. First, its network structure is novel: each layer of the network is a group of mutually independent $k$-centers clusterings. Second, its learning method is extremely simple: the $k$ centers of each clustering are only $k$ randomly selected examples from the training data; for small-scale data sets, the $k$ centers are further randomly reconstructed by a simple cyclic-shift operation. Experimental results on nonlinear dimensionality reduction show that the proposed method can learn abstract representations on both large-scale and small-scale problems, and meanwhile is much faster than deep neural networks on large-scale problems.
[ "Xiao-Lei Zhang", "['Xiao-Lei Zhang']" ]
stat.ML cs.LG
null
1312.4426
null
null
http://arxiv.org/pdf/1312.4426v1
2013-12-16T16:51:51Z
2013-12-16T16:51:51Z
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
In this paper we present two new approaches to efficiently solve large-scale compressed sensing problems. These two ideas are independent of each other and can therefore be used either separately or together. We consider all possibilities. For the first approach, we note that the zero vector can be taken as the initial basic (infeasible) solution for the linear programming problem and therefore, if the true signal is very sparse, some variants of the simplex method can be expected to take only a small number of pivots to arrive at a solution. We implemented one such variant and demonstrate a dramatic improvement in computation time on very sparse signals. The second approach requires a redesigned sensing mechanism in which the vector signal is stacked into a matrix. This allows us to exploit the Kronecker compressed sensing (KCS) mechanism. We show that the Kronecker sensing requires stronger conditions for perfect recovery compared to the original vector problem. However, the Kronecker sensing, modeled correctly, is a much sparser linear optimization problem. Hence, algorithms that benefit from sparse problem representation, such as interior-point methods, can solve the Kronecker sensing problems much faster than the corresponding vector problem. In our numerical studies, we demonstrate a ten-fold improvement in the computation time.
[ "Robert Vanderbei and Han Liu and Lie Wang and Kevin Lin", "['Robert Vanderbei' 'Han Liu' 'Lie Wang' 'Kevin Lin']" ]
cs.LG
null
1312.4461
null
null
http://arxiv.org/pdf/1312.4461v4
2014-01-28T22:29:55Z
2013-12-16T18:58:34Z
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced by Bengio, et. al., where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be ommitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process.
[ "['Andrew Davis' 'Itamar Arel']", "Andrew Davis, Itamar Arel" ]
stat.ML cs.LG stat.ME
null
1312.4479
null
null
http://arxiv.org/pdf/1312.4479v1
2013-12-16T19:38:35Z
2013-12-16T19:38:35Z
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated with multitype branching processes), sociology and econometrics. We focus on I) Identifying categories that appear simultaneously, or on the contrary that are mutually exclusive. This is achieved by identifying conditional independence relationships between the variables; II)Building parsimonious parametric models consistent with these relationships; III) Characterising and testing the effects of covariates on the joint distribution of the counts. To achieve these goals, we propose an approach based on graphical probabilistic models, and more specifically partially directed acyclic graphs.
[ "Pierre Fernique (VP, AGAP), Jean-Baptiste Durand (VP, INRIA Grenoble\n Rh\\^one-Alpes / LJK Laboratoire Jean Kuntzmann), Yann Gu\\'edon (VP, AGAP)", "['Pierre Fernique' 'Jean-Baptiste Durand' 'Yann Guédon']" ]
cs.LG stat.ML
null
1312.4527
null
null
http://arxiv.org/pdf/1312.4527v1
2013-12-16T09:34:43Z
2013-12-16T09:34:43Z
Probable convexity and its application to Correlated Topic Models
Non-convex optimization problems often arise from probabilistic modeling, such as estimation of posterior distributions. Non-convexity makes the problems intractable, and poses various obstacles for us to design efficient algorithms. In this work, we attack non-convexity by first introducing the concept of \emph{probable convexity} for analyzing convexity of real functions in practice. We then use the new concept to analyze an inference problem in the \emph{Correlated Topic Model} (CTM) and related nonconjugate models. Contrary to the existing belief of intractability, we show that this inference problem is concave under certain conditions. One consequence of our analyses is a novel algorithm for learning CTM which is significantly more scalable and qualitative than existing methods. Finally, we highlight that stochastic gradient algorithms might be a practical choice to resolve efficiently non-convex problems. This finding might find beneficial in many contexts which are beyond probabilistic modeling.
[ "['Khoat Than' 'Tu Bao Ho']", "Khoat Than and Tu Bao Ho" ]
stat.ML cs.LG
null
1312.4551
null
null
http://arxiv.org/pdf/1312.4551v1
2013-12-16T21:03:28Z
2013-12-16T21:03:28Z
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
We present an asymptotic analysis of Viterbi Training (VT) and contrast it with a more conventional Maximum Likelihood (ML) approach to parameter estimation in Hidden Markov Models. While ML estimator works by (locally) maximizing the likelihood of the observed data, VT seeks to maximize the probability of the most likely hidden state sequence. We develop an analytical framework based on a generating function formalism and illustrate it on an exactly solvable model of HMM with one unambiguous symbol. For this particular model the ML objective function is continuously degenerate. VT objective, in contrast, is shown to have only finite degeneracy. Furthermore, VT converges faster and results in sparser (simpler) models, thus realizing an automatic Occam's razor for HMM learning. For more general scenario VT can be worse compared to ML but still capable of correctly recovering most of the parameters.
[ "['Armen E. Allahverdyan' 'Aram Galstyan']", "Armen E. Allahverdyan and Aram Galstyan" ]
stat.ML cs.LG
null
1312.4564
null
null
http://arxiv.org/pdf/1312.4564v4
2014-06-09T09:31:13Z
2013-12-16T21:22:46Z
Adaptive Stochastic Alternating Direction Method of Multipliers
The Alternating Direction Method of Multipliers (ADMM) has been studied for years. The traditional ADMM algorithm needs to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the time complexity, stochastic ADMM algorithms were proposed to replace the expected function with a random loss function associated with one uniformly drawn example plus a Bregman divergence. The Bregman divergence, however, is derived from a simple second order proximal function, the half squared norm, which could be a suboptimal choice. In this paper, we present a new family of stochastic ADMM algorithms with optimal second order proximal functions, which produce a new family of adaptive subgradient methods. We theoretically prove that their regret bounds are as good as the bounds which could be achieved by the best proximal function that can be chosen in hindsight. Encouraging empirical results on a variety of real-world datasets confirm the effectiveness and efficiency of the proposed algorithms.
[ "['Peilin Zhao' 'Jinwei Yang' 'Tong Zhang' 'Ping Li']", "Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li" ]
cs.CV cs.LG cs.NE
null
1312.4569
null
null
http://arxiv.org/pdf/1312.4569v2
2014-03-10T15:34:55Z
2013-11-05T10:45:48Z
Dropout improves Recurrent Neural Networks for Handwriting Recognition
Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequence is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.
[ "['Vu Pham' 'Théodore Bluche' 'Christopher Kermorvant' 'Jérôme Louradour']", "Vu Pham, Th\\'eodore Bluche, Christopher Kermorvant, J\\'er\\^ome\n Louradour" ]
cs.LG
null
1312.4599
null
null
http://arxiv.org/pdf/1312.4599v1
2013-12-17T00:32:43Z
2013-12-17T00:32:43Z
Evolution and Computational Learning Theory: A survey on Valiant's paper
Darwin's theory of evolution is considered to be one of the greatest scientific gems in modern science. It not only gives us a description of how living things evolve, but also shows how a population evolves through time and also, why only the fittest individuals continue the generation forward. The paper basically gives a high level analysis of the works of Valiant[1]. Though, we know the mechanisms of evolution, but it seems that there does not exist any strong quantitative and mathematical theory of the evolution of certain mechanisms. What is defined exactly as the fitness of an individual, why is that only certain individuals in a population tend to mutate, how computation is done in finite time when we have exponentially many examples: there seems to be a lot of questions which need to be answered. [1] basically treats Darwinian theory as a form of computational learning theory, which calculates the net fitness of the hypotheses and thus distinguishes functions and their classes which could be evolvable using polynomial amount of resources. Evolution is considered as a function of the environment and the previous evolutionary stages that chooses the best hypothesis using learning techniques that makes mutation possible and hence, gives a quantitative idea that why only the fittest individuals tend to survive and have the power to mutate.
[ "['Arka Bhattacharya']", "Arka Bhattacharya" ]
stat.ML cs.LG
null
1312.4626
null
null
http://arxiv.org/pdf/1312.4626v1
2013-12-17T03:33:08Z
2013-12-17T03:33:08Z
Compact Random Feature Maps
Kernel approximation using randomized feature maps has recently gained a lot of interest. In this work, we identify that previous approaches for polynomial kernel approximation create maps that are rank deficient, and therefore do not utilize the capacity of the projected feature space effectively. To address this challenge, we propose compact random feature maps (CRAFTMaps) to approximate polynomial kernels more concisely and accurately. We prove the error bounds of CRAFTMaps demonstrating their superior kernel reconstruction performance compared to the previous approximation schemes. We show how structured random matrices can be used to efficiently generate CRAFTMaps, and present a single-pass algorithm using CRAFTMaps to learn non-linear multi-class classifiers. We present experiments on multiple standard data-sets with performance competitive with state-of-the-art results.
[ "Raffay Hamid and Ying Xiao and Alex Gittens and Dennis DeCoste", "['Raffay Hamid' 'Ying Xiao' 'Alex Gittens' 'Dennis DeCoste']" ]
cs.LG cs.SD q-bio.NC
null
1312.4695
null
null
http://arxiv.org/pdf/1312.4695v3
2014-02-18T10:20:25Z
2013-12-17T09:12:55Z
Sparse, complex-valued representations of natural sounds learned with phase and amplitude continuity priors
Complex-valued sparse coding is a data representation which employs a dictionary of two-dimensional subspaces, while imposing a sparse, factorial prior on complex amplitudes. When trained on a dataset of natural image patches, it learns phase invariant features which closely resemble receptive fields of complex cells in the visual cortex. Features trained on natural sounds however, rarely reveal phase invariance and capture other aspects of the data. This observation is a starting point of the present work. As its first contribution, it provides an analysis of natural sound statistics by means of learning sparse, complex representations of short speech intervals. Secondly, it proposes priors over the basis function set, which bias them towards phase-invariant solutions. In this way, a dictionary of complex basis functions can be learned from the data statistics, while preserving the phase invariance property. Finally, representations trained on speech sounds with and without priors are compared. Prior-based basis functions reveal performance comparable to unconstrained sparse coding, while explicitely representing phase as a temporal shift. Such representations can find applications in many perceptual and machine learning tasks.
[ "['Wiktor Mlynarski']", "Wiktor Mlynarski" ]
cs.LG
null
1312.4986
null
null
http://arxiv.org/pdf/1312.4986v1
2013-12-17T22:12:52Z
2013-12-17T22:12:52Z
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Several machine learning techniques treat instances in a data set differently during training such as curriculum learning, filtering, and boosting. However, an automated method for determining how beneficial an instance is for inferring a model of the data does not exist. In this paper, we present an automated method that orders the instances in a data set by complexity based on the their likelihood of being misclassified (instance hardness). The underlying assumption of this method is that instances with a high likelihood of being misclassified represent more complex concepts in a data set. Ordering the instances in a data set allows a learning algorithm to focus on the most beneficial instances and ignore the detrimental ones. We compare ordering the instances in a data set in curriculum learning, filtering and boosting. We find that ordering the instances significantly increases classification accuracy and that filtering has the largest impact on classification accuracy. On a set of 52 data sets, ordering the instances increases the average accuracy from 81% to 84%.
[ "['Michael R. Smith' 'Tony Martinez']", "Michael R. Smith and Tony Martinez" ]
cs.LG
null
1312.5021
null
null
http://arxiv.org/pdf/1312.5021v1
2013-12-18T02:10:21Z
2013-12-18T02:10:21Z
Efficient Online Bootstrapping for Large Scale Learning
Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized datasets, due to its high computation cost. This work describes a highly scalable online bootstrapping strategy, implemented inside Vowpal Wabbit, that is several times faster than traditional strategies. Our experiments indicate that, in addition to providing a black box-like method for estimating uncertainty, our implementation of online bootstrapping may also help to train models with better prediction performance due to model averaging.
[ "['Zhen Qin' 'Vaclav Petricek' 'Nikos Karampatziakis' 'Lihong Li'\n 'John Langford']", "Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John\n Langford" ]
stat.ML cs.LG math.OC
null
1312.5023
null
null
http://arxiv.org/pdf/1312.5023v1
2013-12-18T02:21:01Z
2013-12-18T02:21:01Z
Contextually Supervised Source Separation with Application to Energy Disaggregation
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We analyze the case of $\ell_2$ loss theoretically and show that recovery of the signal components depends only on cross-correlation between features for different signals, not on correlations between features for the same signal. Contextually supervised source separation is a natural fit for domains with large amounts of data but no explicit supervision; our motivating application is energy disaggregation of hourly smart meter data (the separation of whole-home power signals into different energy uses). Here we apply contextual supervision to disaggregate the energy usage of thousands homes over four years, a significantly larger scale than previously published efforts, and demonstrate on synthetic data that our method outperforms the unsupervised approach.
[ "['Matt Wytock' 'J. Zico Kolter']", "Matt Wytock and J. Zico Kolter" ]
stat.AP cs.LG stat.ML
null
1312.5124
null
null
http://arxiv.org/pdf/1312.5124v1
2013-12-18T13:13:39Z
2013-12-18T13:13:39Z
Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix
Non-Negative Matrix Factorization, NMF, attempts to find a number of archetypal response profiles, or parts, such that any sample profile in the dataset can be approximated by a close profile among these archetypes or a linear combination of these profiles. The non-negativity constraint is imposed while estimating archetypal profiles, due to the non-negative nature of the observed signal. Apart from non negativity, a volume constraint can be applied on the Score matrix W to enhance the ability of learning parts of NMF. In this report, we describe a very simple algorithm, which in effect achieves volume minimization, although indirectly.
[ "Paul Fogel", "['Paul Fogel']" ]
stat.ML cs.LG math.OC
null
1312.5179
null
null
http://arxiv.org/pdf/1312.5179v1
2013-12-18T15:35:32Z
2013-12-18T15:35:32Z
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hypergraphs via graphs or on tensor methods which are only applicable under special conditions. In this paper, we present a new learning framework on hypergraphs which fully uses the hypergraph structure. The key element is a family of regularization functionals based on the total variation on hypergraphs.
[ "['Matthias Hein' 'Simon Setzer' 'Leonardo Jost' 'Syama Sundar Rangapuram']", "Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram" ]
stat.ML cs.LG math.OC
null
1312.5192
null
null
http://arxiv.org/pdf/1312.5192v2
2014-03-24T10:10:42Z
2013-12-18T16:01:25Z
Nonlinear Eigenproblems in Data Analysis - Balanced Graph Cuts and the RatioDCA-Prox
It has been recently shown that a large class of balanced graph cuts allows for an exact relaxation into a nonlinear eigenproblem. We review briefly some of these results and propose a family of algorithms to compute nonlinear eigenvectors which encompasses previous work as special cases. We provide a detailed analysis of the properties and the convergence behavior of these algorithms and then discuss their application in the area of balanced graph cuts.
[ "Leonardo Jost, Simon Setzer, Matthias Hein", "['Leonardo Jost' 'Simon Setzer' 'Matthias Hein']" ]
cs.LG cs.AI cs.CL stat.ML
null
1312.5198
null
null
http://arxiv.org/pdf/1312.5198v4
2014-04-25T13:31:53Z
2013-12-18T16:13:08Z
Learning Semantic Script Knowledge with Event Embeddings
Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.
[ "Ashutosh Modi and Ivan Titov", "['Ashutosh Modi' 'Ivan Titov']" ]
cs.CV cs.LG cs.NE
null
1312.5242
null
null
http://arxiv.org/pdf/1312.5242v3
2014-02-16T13:07:23Z
2013-12-18T17:44:17Z
Unsupervised feature learning by augmenting single images
When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main component of an unsupervised feature learning architecture. To that end we sample a set of random image patches and declare each of them to be a separate single-image surrogate class. We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches. Finally we train a convolutional neural network to discriminate between these surrogate classes. The feature representation learned by the network can then be used in various vision tasks. We find that this simple feature learning algorithm is surprisingly successful, achieving competitive classification results on several popular vision datasets (STL-10, CIFAR-10, Caltech-101).
[ "['Alexey Dosovitskiy' 'Jost Tobias Springenberg' 'Thomas Brox']", "Alexey Dosovitskiy, Jost Tobias Springenberg and Thomas Brox" ]
stat.ML cs.LG
null
1312.5258
null
null
http://arxiv.org/pdf/1312.5258v2
2014-10-24T19:16:14Z
2013-12-18T18:30:51Z
On the Challenges of Physical Implementations of RBMs
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the cost of sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are designed to reproduce aspects of the D-Wave quantum computer, but the issues we investigate arise in most forms of physical computation.
[ "Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville, Yoshua Bengio", "['Vincent Dumoulin' 'Ian J. Goodfellow' 'Aaron Courville' 'Yoshua Bengio']" ]
cs.CE cs.LG
null
1312.5354
null
null
http://arxiv.org/pdf/1312.5354v2
2014-07-28T09:44:01Z
2013-12-18T22:08:07Z
Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces
We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement. We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.
[ "['Yaqub Alwan' 'Zoran Cvetkovic' 'Michael Curtis']", "Yaqub Alwan, Zoran Cvetkovic, Michael Curtis" ]
cs.NE cs.LG stat.ML
null
1312.5394
null
null
http://arxiv.org/pdf/1312.5394v1
2013-12-19T02:38:40Z
2013-12-19T02:38:40Z
Missing Value Imputation With Unsupervised Backpropagation
Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods.
[ "['Michael S. Gashler' 'Michael R. Smith' 'Richard Morris' 'Tony Martinez']", "Michael S. Gashler, Michael R. Smith, Richard Morris, Tony Martinez" ]
cs.LG stat.ML
null
1312.5398
null
null
http://arxiv.org/pdf/1312.5398v2
2014-02-17T20:32:00Z
2013-12-19T03:24:58Z
Continuous Learning: Engineering Super Features With Feature Algebras
In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the non-linear features become 2^N -degree polynomials on the original space. We show that in a limit of an infinite number of iterations derived non-linear features must form an associative algebra: a product of two features is equal to a linear combination of features from the same feature space for any given input point. Because each iteration consists of solving a series of convex problems that contain all previous solutions, the likelihood of the models in the sequence is increasing with each iteration while the dimension of the model parameter space is set to a limited controlled value.
[ "Michael Tetelman", "['Michael Tetelman']" ]
stat.ML cs.LG
null
1312.5412
null
null
http://arxiv.org/pdf/1312.5412v3
2014-01-06T20:07:09Z
2013-12-19T05:37:50Z
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images
We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fitting. GRBMs are widely considered as an unsuitable model for natural images because they gain non-overcomplete representations which include uniform filters that do not represent useful image features. We have recently found that GRBMs once gain and subsequently lose useful filters during their training, contrary to this common perspective. We attribute this phenomenon to a tradeoff between overcompleteness of GRBM representations and data fitting. To gain GRBM representations that are overcomplete and fit data well, we propose a measure for GRBM representation quality, approximated mutual information, and an early stopping technique based on this measure. The proposed method boosts performance of classifiers trained on GRBM representations.
[ "Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara", "['Taichi Kiwaki' 'Takaki Makino' 'Kazuyuki Aihara']" ]
cs.LG
10.1007/978-3-662-44851-9_28
1312.5419
null
null
http://arxiv.org/abs/1312.5419v3
2014-05-15T11:32:03Z
2013-12-19T06:53:24Z
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform state-of-the-art approaches on six large-scale textual datasets with diverse characteristics.
[ "['Jinseok Nam' 'Jungi Kim' 'Eneldo Loza Mencía' 'Iryna Gurevych'\n 'Johannes Fürnkranz']", "Jinseok Nam, Jungi Kim, Eneldo Loza Menc\\'ia, Iryna Gurevych, Johannes\n F\\\"urnkranz" ]
cs.SY cs.IT cs.LG math.IT math.OC
null
1312.5434
null
null
http://arxiv.org/pdf/1312.5434v3
2014-12-16T08:16:46Z
2013-12-19T08:29:57Z
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis
In this work and the supporting Parts II [2] and III [3], we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We examine asynchronous networks that are subject to fairly general sources of uncertainties, such as changing topologies, random link failures, random data arrival times, and agents turning on and off randomly. Under this model, agents in the network may stop updating their solutions or may stop sending or receiving information in a random manner and without coordination with other agents. We establish in Part I conditions on the first and second-order moments of the relevant parameter distributions to ensure mean-square stable behavior. We derive in Part II expressions that reveal how the various parameters of the asynchronous behavior influence network performance. We compare in Part III the performance of asynchronous networks to the performance of both centralized solutions and synchronous networks. One notable conclusion is that the mean-square-error performance of asynchronous networks shows a degradation only of the order of $O(\nu)$, where $\nu$ is a small step-size parameter, while the convergence rate remains largely unaltered. The results provide a solid justification for the remarkable resilience of cooperative networks in the face of random failures at multiple levels: agents, links, data arrivals, and topology.
[ "['Xiaochuan Zhao' 'Ali H. Sayed']", "Xiaochuan Zhao and Ali H. Sayed" ]
cs.SY cs.IT cs.LG math.IT math.OC
null
1312.5438
null
null
http://arxiv.org/pdf/1312.5438v3
2014-12-16T08:28:11Z
2013-12-19T08:39:58Z
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis
In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for asynchronous events over adaptive networks including random topologies, random link failures, random data arrival times, and agents turning on and off randomly. We performed a stability analysis and established the notable fact that the network is still able to converge in the mean-square-error sense to the desired solution. Once stable behavior is guaranteed, it becomes important to evaluate how fast the iterates converge and how close they get to the optimal solution. This is a demanding task due to the various asynchronous events and due to the fact that agents influence each other. In this Part II, we carry out a detailed analysis of the mean-square-error performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We derive analytical expressions for the mean-square convergence rate and the steady-state mean-square-deviation. The expressions reveal how the various parameters of the asynchronous behavior influence network performance. In the process, we establish the interesting conclusion that even under the influence of asynchronous events, all agents in the adaptive network can still reach an $O(\nu^{1 + \gamma_o'})$ near-agreement with some $\gamma_o' > 0$ while approaching the desired solution within $O(\nu)$ accuracy, where $\nu$ is proportional to the small step-size parameter for adaptation.
[ "['Xiaochuan Zhao' 'Ali H. Sayed']", "Xiaochuan Zhao and Ali H. Sayed" ]
cs.SY cs.IT cs.LG math.IT math.OC
null
1312.5439
null
null
http://arxiv.org/pdf/1312.5439v3
2014-12-16T08:31:57Z
2013-12-19T08:45:42Z
Asynchronous Adaptation and Learning over Networks - Part III: Comparison Analysis
In Part II [3] we carried out a detailed mean-square-error analysis of the performance of asynchronous adaptation and learning over networks under a fairly general model for asynchronous events including random topologies, random link failures, random data arrival times, and agents turning on and off randomly. In this Part III, we compare the performance of synchronous and asynchronous networks. We also compare the performance of decentralized adaptation against centralized stochastic-gradient (batch) solutions. Two interesting conclusions stand out. First, the results establish that the performance of adaptive networks is largely immune to the effect of asynchronous events: the mean and mean-square convergence rates and the asymptotic bias values are not degraded relative to synchronous or centralized implementations. Only the steady-state mean-square-deviation suffers a degradation in the order of $\nu$, which represents the small step-size parameters used for adaptation. Second, the results show that the adaptive distributed network matches the performance of the centralized solution. These conclusions highlight another critical benefit of cooperation by networked agents: cooperation does not only enhance performance in comparison to stand-alone single-agent processing, but it also endows the network with remarkable resilience to various forms of random failure events and is able to deliver performance that is as powerful as batch solutions.
[ "['Xiaochuan Zhao' 'Ali H. Sayed']", "Xiaochuan Zhao and Ali H. Sayed" ]
cs.IR cs.LG cs.MM
10.1109/TASLP.2014.2337842
1312.5457
null
null
http://arxiv.org/abs/1312.5457v1
2013-12-19T09:40:03Z
2013-12-19T09:40:03Z
Codebook based Audio Feature Representation for Music Information Retrieval
Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on \emph{content based} methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the \emph{audio content representation}. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed \emph{codebook} and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely \emph{the LASSO}, \emph{vector quantization (VQ)} and \emph{cosine similarity (CS)}. We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-$\tau$ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.
[ "['Yonatan Vaizman' 'Brian McFee' 'Gert Lanckriet']", "Yonatan Vaizman, Brian McFee and Gert Lanckriet" ]
cs.LG stat.ML
null
1312.5465
null
null
http://arxiv.org/pdf/1312.5465v3
2014-09-25T02:31:30Z
2013-12-19T10:10:02Z
Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use. It is known that different $q$ leads to different properties of the deduced estimators, say, $l^2$ regularization leads to smooth estimators while $l^1$ regularization leads to sparse estimators. Then, how does the generalization capabilities of $l^q$ regularization learning vary with $q$? In this paper, we study this problem in the framework of statistical learning theory and show that implementing $l^q$ coefficient regularization schemes in the sample dependent hypothesis space associated with Gaussian kernel can attain the same almost optimal learning rates for all $0<q<\infty$. That is, the upper and lower bounds of learning rates for $l^q$ regularization learning are asymptotically identical for all $0<q<\infty$. Our finding tentatively reveals that, in some modeling contexts, the choice of $q$ might not have a strong impact with respect to the generalization capability. From this perspective, $q$ can be arbitrarily specified, or specified merely by other no generalization criteria like smoothness, computational complexity, sparsity, etc..
[ "Shaobo Lin, Jinshan Zeng, Jian Fang and Zongben Xu", "['Shaobo Lin' 'Jinshan Zeng' 'Jian Fang' 'Zongben Xu']" ]