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

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3 Author(s)
Boutemedjet, S. ; Dept. d''lnformatique, Univ. de Sherbrooke, Sherbrooke, QC ; Bouguila, N. ; Ziou, D.

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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:31 ,  Issue: 8 )

Date of Publication:

Aug. 2009

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