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

被引：99

作者：

Boutemedjet, Sabri ^{[1
]}

Bouguila, Nizar ^{[2
]}

Ziou, Djemel ^{[1
]}

机构：

[1] Univ Sherbrooke, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada

[2] Concordia Univ, Concordia Inst Informat Engn CIISE, Montreal, PQ H3G 1T7, Canada

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2009年 / 31卷 / 08期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Unsupervised learning; mixture models; feature selection; dimensionality reduction; generalized Dirichlet mixture; EM; MML; information theory; object image categorization; STATISTICAL PATTERN-RECOGNITION; DIRICHLET MIXTURE MODEL; UNSUPERVISED SELECTION;

D O I：

10.1109/TPAMI.2008.155

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1429 / 1443

页数：15

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