Greedy approximation of kernel PCA by minimizing the mapping error

Authors

Peng Cheng, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Philip Ogunbona, University of WollongongFollow

RIS ID

32112

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.