Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14640
Title: Essence of kernel Fisher discriminant : KPCA plus LDA
Authors: Yang, J
Jin, Z
Yang, JY
Zhang, D 
Frangi, AF
Keywords: Feature extraction
Fisher linear discriminant analysis
Handwritten numeral recognition
Kernel-based methods
Principal component analysis
Issue Date: 2004
Source: Pattern recognition, 2004, v. 37, no. 10, p. 2097-2100 How to cite?
Journal: Pattern Recognition 
Abstract: 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.
URI: http://hdl.handle.net/10397/14640
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2003.10.015
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