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Presentation
Greedy approximation of kernel PCA by minimizing the mapping error
Faculty of Informatics - Papers (Archive)
  • Peng Cheng, University of Wollongong
  • Wanqing Li, University of Wollongong
  • Philip Ogunbona, University of Wollongong
RIS ID
32112
Publication Date
1-1-2009
Publication Details

Cheng, P., Li, W. & Ogunbona, P. (2009). Greedy approximation of kernel PCA by minimizing the mapping error. 2009 Digital Image Computing: Techniques and Applications (pp. 303-308). Melbourne, Australia: IEEE.

Abstract

In this paper we propose a new kernel PCA (KPCA) speed-up algorithm that aims to find a reduced KPCA to approximate the kernel mapping. The algorithm works by greedily choosing a subset of the training samples that minimizes the mean square error of the kernel mapping between the original KPCA and the reduced KPCA. Experimental results have shown that the proposed algorithm is more efficient in computation and effective with lower mapping errors than previous algorithms.

Citation Information
Peng Cheng, Wanqing Li and Philip Ogunbona. "Greedy approximation of kernel PCA by minimizing the mapping error" (2009) p. 303 - 308
Available at: http://works.bepress.com/p_ogunbona/85/