A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering

Authors:
Sabri Boutemedjet;Nizar Bouguila;Djemel Ziou
Affiliations:
Université de Sherbrooke, Sherbrooke;Concordia University, Montreal;Université de Sherbrooke, Sherbrooke
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

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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.