Letters: A method for speeding up feature extraction based on KPCA

Authors:
Yong Xu;David Zhang;Fengxi Song;Jing-Yu Yang;Zhong Jing;Miao Li
Affiliations:
Bio-Computing Research Center, Shenzhen graduate school, Harbin Institute of Technology, Shenzhen 518055, China;The Biometrics Research Center and Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;Bio-Computing Research Center, Shenzhen graduate school, Harbin Institute of Technology, Shenzhen 518055, China;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China;College of Computer Science & Technology, Harbin Institute of Technology, Harbin, China
Venue:
Neurocomputing
Year:
2007

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Abstract

Kernel principal component analysis (KPCA) extracts features of samples with an efficiency in inverse proportion to the size of the training sample set. In this paper, we develop a novel method to improve KPCA-based feature extraction. The developed method is the first one that is methodologically consistent with KPCA. Experiments on several benchmark datasets illustrate that the feature extraction process derived from the novel method is much more efficient than that associated with KPCA. Moreover, the classification accuracy generated from the developed method is similar to that of KPCA.