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Proceedings Paper

A comparative study of two kernel ideas for nonlinear feature extraction
Author(s): Cheng Yang; Jufu Feng
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Paper Abstract

Using the kernel trick idea and the kernels as features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be employed to extract nonlinear features. Thus, we have two approaches to transform an existing linear feature extraction algorithm into its nonlinear counterpart. It has been proved that they are equivalent up to different scalings on each feature by rigorous theoretical analysis. In this paper, we perform experiments on several benchmark datasets and give a comparative study of the two kernel ideas applied to certain feature extraction algorithms such as linear discriminant analysis and principal component analysis. These results provide a better understanding of the kernel method.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961O (30 October 2009); doi: 10.1117/12.833591
Show Author Affiliations
Cheng Yang, Peking Univ. (China)
Jufu Feng, Peking Univ. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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