Title: Discriminant analysis in pairwise kernel learning for SVM classification

Authors: Hao Jiang; Wai-Ki Ching; Delin Chu

Addresses: Advanced Modeling and Applied Computing Laboratory Department of Mathematics, University of Hong Kong, Pokfulam Road, Hong Kong ' Advanced Modeling and Applied Computing Laboratory Department of Mathematics, University of Hong Kong, Pokfulam Road, Hong Kong ' Department of Mathematics, National University of Singapore, Science Drive 2, 117543 Singapore

Abstract: Multiple kernel learning arises when different types of kernels are employed simultaneously. In particular, in the situation that the data are from heterogeneous sources. In this study, we developed a new framework for determining the coefficients in learning pairwise kernels for classification in Support Vector Machines (SVM). The effectiveness of the proposed method was then demonstrated through the prediction of self-renewal and pluripotency mESCs stemness membership genes. It was also tested on the power of discrimination in DNA repair gene data. The promising formulation in learning coefficients for pairwise kernel learning was shown via experimental evaluation. This may provide a novel perspective for kernel learning in future applications.

Keywords: discriminant analysis; kernel learning; SVM; support vector machines; classification; pairwise kernels; DNA repair; bioinformatics.

DOI: 10.1504/IJBRA.2012.048963

International Journal of Bioinformatics Research and Applications, 2012 Vol.8 No.3/4, pp.305 - 321

Published online: 05 Dec 2014 *

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