The mixture of multivariate Bernoulli distributions (MMB) is a statistical model for high-dimensional binary data in widespread use. Recently, the MMB has been used to model the sequence of packet receptions and losses of wireless links in sensor networks. Given an MMB trained on long data traces recorded from links of a deployed network, one can then use samples from the MMB to test different routing algorithms for as long as desired. However, learning an accurate model for a new link requires collecting from it long traces over periods of hours, a costly process in practice (e.g. limited battery life). We propose an algorithm that can adapt a preexisting MMB trained with extensive data to a new link from which very limited data is available. Our approach constrains the new MMB's parameters through a nonlinear transformation of the existing MMB's parameters. The transformation has a small number of parameters that are estimated using a generalized EM algorithm with an inner loop of BFGS iterations. We demonstrate the efficacy of the approach using the MNIST dataset of handwritten digits, and wireless link data from a sensor network. We show we can learn accurate models from data traces of about 1 minute, about 10 times shorter than needed if training an MMB from scratch.

In this paper, we propose a novel approach for learning regression rules by transforming the regression problem into a classification problem. Unlike previous approaches to regression by classification, in our approach the discretization of the class variable is tightly integrated into the rule learning algorithm. The key idea is to dynamically define a region around the target value predicted by the rule, and considering all examples within that region as positive and all examples outside that region as negative. In this way, conventional rule learning heuristics may be used for inducing regression rules. Our results show that our heuristic algorithm outperforms approaches that use a static discretization of the target variable, and performs en par with other comparable rule-based approaches, albeit without reaching the performance of statistical approaches.

There are a number of papers that use hill-climbing or k-We introduce a new nearest neighbor search algorithm. NN graphs for nearest neighbor search, but to the best of our The algorithm builds a nearest neighbor knowledge, using hill-climbing on k-NN graphs is a new idea.

Most of the sequential patterns extraction methods proposed so far deal with patterns composed of events linked by temporal relationships based on simple precedence between instants. In many real situations, some quantitative information about event duration or inter-event delay is necessary to discriminate phenomena. We propose the algorithm QTIPrefixSpan for extracting temporal patterns composed of events to which temporal intervals describing their position in time and their duration are associated. It extends algorithm PrefixSpan with a multi-dimensional interval clustering step for extracting the representative temporal intervals associated to events in patterns. Experiments on simulated data show that our algorithm is efficient for extracting precise patterns even in noisy contexts and that it improves the performance of a former algorithm which used a clustering method based on the EM algorithm.

In this paper, we tackle the challenges of multi-label classification by developing a general conditional dependency network model. The proposed model is a cyclic directed graphical model, which provides an intuitive representation for the dependencies among multiple label variables, and a well integrated framework for efficient model training using binary classifiers and label predictions using Gibbs sampling inference. Our experiments show the proposed conditional model can effectively exploit the label dependency to improve multi-label classification performance.

Dimensionality reduction is a very important topic in machine learning. It can be generally classified into two categories: feature selection and subspace learning. In the past decades, many methods have been proposed for dimensionality reduction. However, most of these works study feature selection and subspace learning independently. In this paper, we present a framework for joint feature selection and subspace learning. We reformulate the subspace learning problem and use L {2,1} -norm on the projection matrix to achieve row-sparsity, which leads to selecting relevant features and learning transformation simultaneously. We discuss two situations of the proposed framework, and present their optimization algorithms. Experiments on benchmark face recognition data sets illustrate that the proposed framework outperforms the state of the art methods overwhelmingly.

Combining graph regularization with nonnegative matrix (tri-)factorization (NMF) has shown great performance improvement compared with traditional nonnegative matrix (tri-)factorization models due to its ability to utilize the geometric structure of the documents and words. In this paper, we show that these models are not well-defined and suffering from trivial solution and scale transfer problems. In order to solve these common problems, we propose two models for graph regularized nonnegative matrix (tri-)factorization, which can be applied for document clustering and co-clustering respectively. In the proposed models, a Normalized Cut-like constraint is imposed on the cluster assignment matrix to make the optimization problem well-defined. We derive a multiplicative updating algorithm for the proposed models, and prove its convergence. Experiments of clustering and co-clustering on benchmark text data sets demonstratethat the proposed models outperform the originalmodels as well as many other state-of-the-art clustering methods.

The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e.g., active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2.0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost.

Learning distance metrics is a fundamental problem in machine learning. Previous distance-metric learning research assumes that the training and test data are drawn from the same distribution, which may be violated in practical applications. When the distributions differ, a situation referred to as covariate shift, the metric learned from training data may not work well on the test data. In this case the metric is said to be inconsistent. In this paper, we address this problem by proposing a novel metric learning framework known as consistent distance metric learning (CDML), which solves the problem under covariate shift situations. We theoretically analyze the conditions when the metrics learned under covariate shift are consistent. Based on the analysis, a convex optimization problem is proposed to deal with the CDML problem. An importance sampling method is proposed for metric learning and two importance weighting strategies are proposed and compared in this work. Experiments are carried out on synthetic and real world datasets to show the effectiveness of the proposed method.

In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.