A comparative study of two kernel ideas for nonlinear feature extraction

Cheng Yang; Jufu Feng

doi:10.1117/12.833591

30 October 2009 A comparative study of two kernel ideas for nonlinear feature extraction

Cheng Yang, Jufu Feng

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961O (2009) https://doi.org/10.1117/12.833591
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Cheng Yang and Jufu Feng "A comparative study of two kernel ideas for nonlinear feature extraction", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961O (30 October 2009); https://doi.org/10.1117/12.833591

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KEYWORDS

Feature extraction

Principal component analysis

Databases

Algorithms

Pattern recognition

Detection and tracking algorithms

Distance measurement

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