Alternative linear discriminant classifier

被引：24

作者：

Chen, SC ^{[1
]}

Yang, XB ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Peoples R China

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 07期

关键词：

fisher linear discriminant analysis; feature extraction; alternative linear discriminant classifier; support vector machines;

D O I：

10.1016/j.patcog.2003.11.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fisher linear discriminant analysis (FLDA) finds a set of optimal discriminating vectors by maximizing Fisher criterion, i.e., the ratio of the between scatter to the within scatter. One of its major disadvantages is that the number of its discriminating vectors capable to be found is bounded from above by C-I for C-class problem. In this paper for binary-class problem, we propose alternative FLDA to breakthrough this limitation by only replacing the original between scatter with a new scatter measure. The experimental results show that our approach give impressive recognition performances compared to both the Fisher approach and linear SVM. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1545 / 1547

页数：3