NDLI: Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference

Content Provider	IEEE Xplore Digital Library
Author	Wentao Fan Bouguila, N.
Copyright Year	2012
Abstract	A large class of problems can be formulated in terms of the clustering process. Mixture models are an increasingly important tool in statistical pattern recognition and for analyzing and clustering complex data. Two challenging aspects that should be addressed when considering mixture models are how to choose between a set of plausible models and how to estimate the model's parameters. In this paper, we address both problems simultaneously within a unified online nonparametric Bayesian framework that we develop to learn a Dirichlet process mixture of Beta-Liouville distributions (i.e., an infinite Beta-Liouville mixture model). The proposed infinite model is used for the online modeling and clustering of proportional data for which the Beta-Liouville mixture has been shown to be effective. We propose a principled approach for approximating the intractable model's posterior distribution by a tractable one-which we develop-such that all the involved mixture's parameters can be estimated simultaneously and effectively in a closed form. This is done through variational inference that enjoys important advantages, such as handling of unobserved attributes and preventing under or overfitting; we explain that in detail. The effectiveness of the proposed work is evaluated on three challenging real applications, namely facial expression recognition, behavior modeling and recognition, and dynamic textures clustering.
Page Count	13
File Size	1844109
Starting Page	1850
Ending Page	1862
File Format	PDF
ISSN	2162237X
Volume Number	24
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	variational inference Bayesian behavior modeling Beta-Liouville distribution Dirichlet process dynamic textures facial expression mixture models nonparametric unsupervised learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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