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

  • Nizar Bouguila
  • Djemel Ziou
  • Jean Vaillancourt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2734)


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.


Expectation Maximization Algorithm Dirichlet Distribution Initialization Method Natural Gradient Wisconsin Breast Cancer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nizar Bouguila
    • 1
  • Djemel Ziou
    • 1
  • Jean Vaillancourt
    • 1
  1. 1.DMI, Faculté des SciencesUniversité de SherbrookeSherbrookeCanada

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