Title :
Greedy Approximation of Kernel PCA by Minimizing the Mapping Error
Author :
Cheng, Peng ; Li, Wanqing ; Ogunbona, Philip
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
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.
Keywords :
data analysis; greedy algorithms; mean square error methods; principal component analysis; greedy approximation; kernel PCA; mapping error minimization; mean square error; speed up algorithm; Application software; Approximation algorithms; Computational efficiency; Computer applications; Computer errors; Digital images; Kernel; Machine learning algorithms; Principal component analysis; Support vector machines; Greedy approximation; Kernel PCA;
Conference_Titel :
Digital Image Computing: Techniques and Applications, 2009. DICTA '09.
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4244-5297-2
Electronic_ISBN :
978-0-7695-3866-2
DOI :
10.1109/DICTA.2009.57
