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|Title: ||KPCA Plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognition|
|Authors: ||Yang, Jian|
Frangi, Alejandro F.
Zhang, David D.
|Subjects: ||Kernel-based methods|
Principal component analysis (PCA)
Fisher linear discriminant analysis (LDA or FLD)
Handwritten digit recognition
|Issue Date: ||Feb-2005 |
|Publisher: ||IEEE Computer Society|
|Citation: ||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.|
|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.|
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|Type: ||Journal/Magazine Article|
|Appears in Collections:||COMP Journal/Magazine Articles|
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