Kernel ICA: An alternative formulation and its application to face recognition |
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Authors: | Jian Yang [Author Vitae] Xiumei Gao [Author Vitae] |
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Affiliation: | a Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong b Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China |
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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. |
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Keywords: | Kernel-based methods Independent component analysis (ICA) Principal component analysis (PCA) Feature extraction Face recognition |
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