A regression approach to LS-SVM and sparse realization based on fast subset selection

Zhang, Jingjing ORCID: https://orcid.org/0000-0002-8970-7568, Li, Kang, Irwin, George W. and Zhao, Wanqing ORCID: https://orcid.org/0000-0001-6160-9547 2012. A regression approach to LS-SVM and sparse realization based on fast subset selection. Presented at: 10th World Congress on Intelligent Control and Automation (WCICA), Beijing, China, 6-8 July 2012. Published in: Cheng, Dai-Zhan ed. Proceedings of the 10th World Congress on Intelligent Control and Automation July 6-8, 2012, Beijing, China. Piscataway, NJ: Institute of Electrical and Electronics Engineers, pp. 612-617. 10.1109/WCICA.2012.6357952

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Abstract

The Least Squares Support Vector Machine (LS-SVM) is a modified SVM with a ridge regression cost function and equality constraints. It has been successfully applied in many classification problems. But, the common issue for LS-SVM is that it lacks sparseness, which is a serious drawback in its applications. To tackle this problem, a fast approach is proposed in this paper for developing sparse LS-SVM. First, a new regression solution is proposed for the LS-SVM which optimizes the same objective function for the conventional solution. Based on this, a new subset selection method is then adopted to realize the sparse approximation. Simulation results on different benchmark datasets i.e. Checkerboard, two Gaussian datasets, show that the proposed solution can achieve better objective value than conventional LS-SVM, and the proposed approach can achieve a more sparse LS-SVM than the conventional LS-SVM while provide comparable predictive classification accuracy. Additionally, the computational complexity is significantly decreased.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISBN:	9781467313971
Last Modified:	27 Oct 2022 09:10
URI:	https://orca.cardiff.ac.uk/id/eprint/64615

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