Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/191
Title: KPCA Plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognition
Authors: Yang, J
Frangi, AF
Yang, JY
Zhang, DD 
Jin, Z
Keywords: Kernel-based methods
Subspace methods
Principal component analysis (PCA)
Fisher linear discriminant analysis (LDA or FLD)
Feature extraction
Machine learning
Face recognition
Handwritten digit recognition
Issue Date: Feb-2005
Publisher: IEEE Computer Society
Source: IEEE transactions on pattern analysis and machine intelligence, Feb. 2005, v. 27, no. 2, p. 230-244 How to cite?
Journal: IEEE transactions on pattern analysis and machine intelligence 
Abstract: This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in “double discriminant subspaces.” The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.
URI: http://hdl.handle.net/10397/191
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2005.33
Rights: © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
165.pdf1.09 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

582
Last Week
0
Last month
5
Citations as of Jun 8, 2016

WEB OF SCIENCETM
Citations

448
Last Week
1
Last month
3
Citations as of Dec 6, 2016

Page view(s)

2,850
Last Week
74
Last month
Checked on Dec 4, 2016

Download(s)

3,866
Checked on Dec 4, 2016

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.