Improved support vector classification using PCA and ICA feature space modification

被引：59

作者：

Fortuna, J ^{[1
]}

Capson, D ^{[1
]}

机构：

[1] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON L8S 4K1, Canada

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 06期

基金：

加拿大自然科学与工程研究理事会;

关键词：

independent component analysis; principal component analysis; support vector machine;

D O I：

10.1016/j.patcog.2003.11.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A Support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to obtain classification results which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation. The performance of the approach is demonstrated with artificial data sets and an example of face recognition from an image database. (C) 2004 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

引用

页码：1117 / 1129

页数：13

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