Sparse least squares support vector machine for function estimation

Authors:
Liang-zhi Gan;Hai-kuan Liu;You-xian Sun
Affiliations:
National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China;Electrical Engineering Department, Xuzhou Normal University, Xuzhou, China;National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Sparse least squares support vector machine (SLS-SVM) for regression is proposed to solve the problem of regression for large sample data. The samples are mapped into Reproducing Kernel Hilbert Space (RKHS) and span a subspace there. Then we could find the basis of the subspace. The basis can represent all the samples linearly. So we can get the least squares support vector machine by solving a small equations set. A numerical example is used to illustrate that this approach can be used to fit nonlinear models for large data set. Being compared with common least squares support vector machine, this method can find sparse solution without any pruning or surgeon, and the computing speed is much faster because the final result is found by solving a small-scale equations set.