EVALUATE DISSIMILARITY OF SAMPLES IN FEATURE SPACE FOR IMPROVING KPCA

Xu Yong (), David Zhang (), Jian Yang, Jin Zhong and Jingyu Yang
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Xu Yong: Shenzhen Graduate School, Harbin Institute of Technology Shenzhen, China
David Zhang: Biometrics Research Centre, Department of Computing The Hong Kong Polytechnic University, Kowloon, Hong Kong
Jian Yang: School of Computer Science & Technology Nanjing University of Science & Technology, Nanjing, China
Jin Zhong: School of Computer Science & Technology Nanjing University of Science & Technology, Nanjing, China
Jingyu Yang: School of Computer Science & Technology Nanjing University of Science & Technology, Nanjing, China

International Journal of Information Technology & Decision Making (IJITDM), 2011, vol. 10, issue 03, 479-495

Abstract: Since in the feature space the eigenvector is a linear combination of all the samples from the training sample set, the computational efficiency of KPCA-based feature extraction falls as the training sample set grows. In this paper, we propose a novel KPCA-based feature extraction method that assumes that an eigenvector can be expressed approximately as a linear combination of a subset of the training sample set ("nodes"). The new method selects maximally dissimilar samples as nodes. This allows the eigenvector to contain the maximum amount of information of the training sample set. By using the distance metric of training samples in the feature space to evaluate their dissimilarity, we devised a very simple and quite efficient algorithm to identify the nodes and to produce the sparse KPCA. The experimental result shows that the proposed method also obtains a high classification accuracy.

Keywords: Feature extraction; kernel methods; kernel PCA (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S0219622011004415

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