Rapid and brief communication: Kernel ICA: An alternative formulation and its application to face recognition

Authors:
Jian Yang;Xiumei Gao;David Zhang;Jing-yu Yang
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong and Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper formulates independent component analysis (ICA) in the kernel-inducing feature space and develops a two-phase kernel ICA algorithm: whitened kernel principal component analysis (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The experiment using a subset of FERET database indicates that the proposed kernel ICA method significantly outperform ICA, PCA and KPCA in terms of the total recognition rate.