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Simultaneous Non-gaussian Data Clustering, Feature Selection and Outliers Rejection

  • Nizar Bouguila
  • Djemel Ziou
  • Sabri Boutemedjet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

A method for simultaneous non-Gaussian data clustering, feature selection and outliers rejection is proposed in this paper. The proposed approach is based on finite generalized Dirichlet mixture models learned within a framework including expectation-maximization updates for model parameters estimation and minimum message length criterion for model selection. Through a challenging application involving texture images discrimination, it is demonstrated that the developed procedure performs effectively in avoiding outliers and selecting relevant features.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nizar Bouguila
    • 1
  • Djemel Ziou
    • 2
  • Sabri Boutemedjet
    • 2
  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada
  2. 2.Département d’InformatiqueUniversité de SherbrookeCanada

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