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Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

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Abstract

The Dirichlet distribution offers high flexibility for modeling data. This paper describes two new mixtures based on this density: the GDD (Generalized Dirichlet Distribution) and the MDD (Multinomial Dirichlet Distribution) mixtures. These mixtures will be used to model continuous and discrete data, respectively. We propose a method for estimating the parameters of these mixtures. The performance of our method is tested by contextual evaluations. In these evaluations we compare the performance of Gaussian and GDD mixtures in the classification of several pattern-recognition data sets and we apply the MDD mixture to the problem of summarizing image databases.

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Author information

Authors and Affiliations

  1. DMI, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Qc, Canada, J1K 2R1

    Nizar Bouguila, Djemel Ziou & Jean Vaillancourt

Authors
  1. Nizar Bouguila
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  2. Djemel Ziou
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  3. Jean Vaillancourt
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Bouguila, N., Ziou, D., Vaillancourt, J. (2003). Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_15

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  • DOI: https://doi.org/10.1007/3-540-45065-3_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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