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An Infinite Mixture of Inverted Dirichlet Distributions

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Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7063))

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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.

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Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, H3G 2W1, Canada

    Taoufik Bdiri & Nizar Bouguila

Authors
  1. Taoufik Bdiri
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  2. Nizar Bouguila
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu  & Liqing Zhang  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

    James Kwok

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Bdiri, T., Bouguila, N. (2011). An Infinite Mixture of Inverted Dirichlet Distributions. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-24958-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24957-0

  • Online ISBN: 978-3-642-24958-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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