Simultaneous non-gaussian data clustering, feature selection and outliers rejection

Authors:
Nizar Bouguila;Djemel Ziou;Sabri Boutemedjet
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc;Département d'Informatique, Université de Sherbrooke, Canada, Qc,;Département d'Informatique, Université de Sherbrooke, Canada, Qc,
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

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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.