A Nonparametric Bayesian Learning Model: Application to Text and Image Categorization

Authors:
Nizar Bouguila;Djemel Ziou
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada H3G 2W1;Département d'Informatique, Université de Sherbrooke, Canada J1K2R1
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

In this paper a nonparametric Bayesian infinite mixture model is introduced. The adoption of this model is motivated by its flexibility. Indeed, it does not require the specification of the number of mixture components to be given in advance and estimates it in a principled manner. Our approach relies on the estimation of the posterior distribution of clusterings using Gibbs sampler. Through applications involving text and image categorization, we show that categorization via infinite mixture models offers a more powerful and robust performance than classic finite mixtures.