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, Jian
Frangi, Alejandro F.
Yang, Jing-yu
Zhang, David D.
Jin, Zhong
Subjects: 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.
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.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/191
DOI: 10.1109/TPAMI.2005.33
ISSN: 0162-8828
Appears in Collections:COMP Journal/Magazine Articles

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