Generalizing relevance weighted LDA

被引：13

作者：

Liang, YX ^{[1
]}

Gong, WG ^{[1
]}

Pan, YJ ^{[1
]}

Li, WH ^{[1
]}

机构：

[1] Chongqing Univ, Educ Minist China, Key Lab Optoelect Technol & Syst, Chongqing 400044, Peoples R China

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 11期

关键词：

linear discriminant analysis; relevance weighted LDA; generalized singular value decomposition; undersampled problem;

D O I：

10.1016/j.patcog.2005.04.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new variant on linear discriminant analysis (LDA) that we refer to as generalizing relevance weighted LDA or GRW-LDA is proposed. GRW-LDA extends the applicability to cases that LDA cannot handle by combining the advantages of two recent LDA enhancements, namely generalized singular value decomposition based LDA and relevance weighted LDA. Experimental results on FERET face database demonstrate the effectiveness of the proposed method. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：2217 / 2219

页数：3

共 5 条

[1] Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J].