Design of a multiple kernel learning algorithm for LS-SVM by convex programming

Authors:
Ling Jian;Zhonghang Xia;Xijun Liang;Chuanhou Gao
Affiliations:
School of Mathematics and Computational Science, China University of Petroleum, Dongying 257061, China and School of Mathematical Science, Dalian University of Technology, Dalian 116024, China;School of Mathematical Science, Dalian University of Technology, Dalian 116024, China and Department of Computer Science, Western Kentucky University, KY 42101-1076, USA;School of Mathematical Science, Dalian University of Technology, Dalian 116024, China;Department of Mathematics, Zhejiang University, Hangzhou 310027, China
Venue:
Neural Networks
Year:
2011

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Abstract

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.