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

Author

Boutemedjet, Sabri ; Bouguila, Nizar ; Ziou, Djemel

Author_Institution

Dept. d´´lnformatique, Univ. de Sherbrooke, Sherbrooke, QC

Volume

31

Issue

8

fYear

2009

Firstpage

1429

Lastpage

1443

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.

Keywords

expectation-maximisation algorithm; feature extraction; image classification; pattern clustering; statistical distributions; unsupervised learning; expectation-maximization algorithm; feature extraction; feature selection; generalized Dirichlet distribution; high-dimensional nonGaussian data clustering; object image categorization; unsupervised learning; Clustering; EM; Feature extraction or construction; MML; Unsupervised learning; and association rules; classification; dimensionality reduction; feature selection; generalized Dirichlet mixture; information theory; mixture models; object image categorization.;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2008.155

Filename

4540103