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A New Approach for High-Dimensional Unsupervised Learning: Applications to Image Restoration

  • Nizar Bouguila
  • Djemel Ziou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

This paper proposes an unsupervised algorithm for learning a high-dimensional finite generalized Dirichlet mixture model. The generalized Dirichlet distribution offers high flexibility and ease of use. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The performance of our method is tested by applying it to the problems of image restoration.

Keywords

Image Restoration Dirichlet Distribution Finite Mixture Model Local Quadratic Convergence Generalize Dirichlet Distribution 
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 2005

Authors and Affiliations

  • Nizar Bouguila
    • 1
  • Djemel Ziou
    • 1
  1. 1.Département d’Informatique, Faculté des SciencesUniversité de SherbrookeSherbrookeCanada

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