Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9228
Title: Kernel ICA : an alternative formulation and its application to face recognition
Authors: Yang, J
Gao, X
Zhang, D 
Yang, JY
Keywords: Face recognition
Feature extraction
Independent component analysis (ICA)
Kernel-based methods
Principal component analysis (PCA)
Issue Date: 2005
Publisher: Elsevier
Source: Pattern recognition, 2005, v. 38, no. 10, p. 1784-1787 How to cite?
Journal: Pattern recognition 
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.
URI: http://hdl.handle.net/10397/9228
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2005.01.023
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