Essence of kernel Fisher discriminant: KPCA plus LDA

被引：139
|
作者：
Yang, J [1 ]
Jin, Z
Yang, JY
Zhang, D
Frangi, AF
机构：
[1] Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R China
[2] Univ Zaragoza, Aragon Inst Engn Res, Comp Vis Grp, E-50018 Zaragoza, Spain
[3] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China
[4] Univ Autonoma Barcelona, Ctr Comp Vis, E-08193 Barcelona, Spain
来源：
PATTERN RECOGNITION | 2004年 / 37卷 / 10期
基金：
中国国家自然科学基金;
关键词：
kernel-based methods; Fisher linear discriminant analysis; principal component analysis; feature extraction; handwritten numeral recognition;
D O I：
10.1016/j.patcog.2003.10.015
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, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2097 / 2100
页数：4
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