Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9931
Title: A method for speeding up feature extraction based on KPCA
Authors: Xu, Y
Zhang, D 
Song, F
Yang, JY
Jing, Z
Li, M
Keywords: Feature extraction
Improved KPCA (IKPCA)
Kernel PCA (KPCA)
Principal component analysis (PCA)
Issue Date: 2007
Publisher: Elsevier
Source: Neurocomputing, 2007, v. 70, no. 4-6, p. 1056-1061 How to cite?
Journal: Neurocomputing 
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.
URI: http://hdl.handle.net/10397/9931
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2006.09.005
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