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
收藏
页码：1429 / 1443
页数：15
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