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

Content Provider	IEEE Xplore Digital Library
Author	Jingjing Zhang Kang Li Irwin, G.W. Wanqing Zhao
Copyright Year	2012
Description	Author affiliation: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5AH, U.K. (Jingjing Zhang; Kang Li; Irwin, G.W.; Wanqing Zhao)
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.
Starting Page	612
Ending Page	617
File Size	183856
Page Count	6
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6357952
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Cost function Training Sparse matrices Accuracy Training data classification Least Squares Support Vector Machines (LS-SVM) sparse regression solution subset selection
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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