Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24372
Title: Fast kernel Fisher discriminant analysis via approximating the kernel principal component analysis
Authors: Wang, J
Li, Q
You, J 
Zhao, Q
Keywords: Fast kernel fisher discriminant analysis
Fisher discriminant analysis
Kernel fisher discriminant analysis
Nonlinear feature extraction
Pattern classification
Issue Date: 2011
Publisher: Elsevier Science Bv
Source: Neurocomputing, 2011, v. 74, no. 17, p. 3313-3322 How to cite?
Journal: Neurocomputing 
Abstract: Kernel Fisher discriminant analysis (KFDA) extracts a nonlinear feature from a sample by calculating as many kernel functions as the training samples. Thus, its computational efficiency is inversely proportional to the size of the training sample set. In this paper we propose a more approach to efficient nonlinear feature extraction, FKFDA (fast KFDA). This FKFDA consists of two parts. First, we select a portion of training samples based on two criteria produced by approximating the kernel principal component analysis (AKPCA) in the kernel feature space. Then, referring to the selected training samples as nodes, we formulate FKFDA to improve the efficiency of nonlinear feature extraction. In FKFDA, the discriminant vectors are expressed as linear combinations of nodes in the kernel feature space, and the extraction of a feature from a sample only requires calculating as many kernel functions as the nodes. Therefore, the proposed FKFDA has a much faster feature extraction procedure compared with the naive kernel-based methods. Experimental results on face recognition and benchmark datasets classification suggest that the proposed FKFDA can generate well classified features.
URI: http://hdl.handle.net/10397/24372
DOI: 10.1016/j.neucom.2011.05.014
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