Why Can SVM Be Performed in PCA Transformed Space for Classification?

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PCA plus SVM is a popular framework for classification problems. On the one hand, it can avoid the SVM kernel Gram to become overlarge and save storage space. On the other hand, PCA plus SVM approach has been verified effective for classification by experience. This paper focuses on building a theoretical foundation for this framework. By derivation, the equivalence relation between PCA plus SVM and LDA is discovered for binary classification. Moreover, we give a specific analysis about the framework to validate our viewpoint compared with SVM and combined SVM/LDA on ORL face database, Yale face database and real world benchmark data. The experimental results indicate that SVM can be performed in PCA transformed space for classification.

Info:

Periodical:

Advanced Materials Research (Volumes 181-182)

Edited by:

Qi Luo and Yuanzhi Wang

Pages:

1031-1037

DOI:

10.4028/www.scientific.net/AMR.181-182.1031

Citation:

J. Zhang et al., "Why Can SVM Be Performed in PCA Transformed Space for Classification?", Advanced Materials Research, Vols. 181-182, pp. 1031-1037, 2011

Online since:

January 2011

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Price:

$35.00

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