CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.08 - August

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Issue No.08 - August (2009 vol.31)

pp: 1429-1443

Sabri Boutemedjet , Université de Sherbrooke, Sherbrooke

Nizar Bouguila , Concordia University, Montreal

Djemel Ziou , Université de Sherbrooke, Sherbrooke

ABSTRACT

This paper presents an unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture model that is able to extract independent and non-Gaussian features without loss of accuracy. The proposed model is learned using the Expectation-Maximization algorithm by minimizing the message length of the data set. Experimental results show the merits of the proposed methodology in the categorization of object images.

INDEX TERMS

Unsupervised learning, mixture models, feature selection, dimensionality reduction, generalized Dirichlet mixture, EM, MML, information theory, object image categorization.

CITATION

Sabri Boutemedjet, Nizar Bouguila, Djemel Ziou, "A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.31, no. 8, pp. 1429-1443, August 2009, doi:10.1109/TPAMI.2008.155REFERENCES