Accelerated Kernel Feature Analysis

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Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An -dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O( n2 ) operations, as compared to O(n3 ) for Sch?olkof, Smola, and M?uller's Kernel Principal Component Analysis (KPCA), and O( 2 n2 ) for Smola, Mangasarian, and Sch?olkopf's Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognit...
Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xing
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xingquan Zhu
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