Efficient Sparse Least Squares Support Vector Machines for Regression

SI Gangquan; SHI Jianquan; Guo Zhang

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Efficient Sparse Least Squares Support Vector Machines for Regression

SI Gangquan 1 * #,SHI Jianquan 2,Guo Zhang 2

1.State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi Province, 710049

2.State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi Province, 710049

*Correspondence author

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Funding: the Specialized Research Fund for Doctoral Program of Higher Education of China （No.Grant No:20130201120011）

Opened online:19 March 2014

Accepted by: none

Citation: SI Gangquan,SHI Jianquan,Guo Zhang.Efficient Sparse Least Squares Support Vector Machines for Regression[OL]. [19 March 2014] http://en.paper.edu.cn/en_releasepaper/content/4588863

To solve the sparseness problem of least squares support vector machine (LSSVM) in learning process, a training algorithm of LSSVM based on active learning is investigated. In the first stage of the algorithm, in order to solve the problem of a large number of similar training data samples, we select support samples by K-means clustering method. The second stage, we obtain a model using LSSVM and conduct function estimation of the all samples, calculating the error of the estimation values and the original samples, sorting support samples and selecting the best sample. Then the selected sample is added into training set to obtain new model. And the processes are repeated until the predetermined performance requirements are achieved, thus the sparse LSSVM model is obtained. The simulation on sinc function indicates that the proposed method performs more effectively than Suykens standard sparse method for removing the redundant support vector with better sparseness and robustness. The experiments on motorcycle dataset of the UCI indicate that the proposed algorithm can solve the problem of heteroscedasticity in some degree.

Keywords:least squares support vector machines; sparse; active learning; K-means clustering

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