An infinite mixture of inverted dirichlet distributions

Authors:
Taoufik Bdiri;Nizar Bouguila
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

In this paper we present an infinite mixture model based on inverted Dirichlet distributions. The proposed mixture is learned using a fully Bayesian approach and allows to overcome a challenging issue when dealing with data clustering namely the automatic selection of the number of clusters. We explore the performance of the proposed approach on the challenging problem of text categorization. The results show that the proposed approach is effective for positive data modeling when compared to those reported using infinite Gaussian mixture.