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CLC number: TP18

On-line Access: 2013-09-05

Received: 2013-01-27

Revision Accepted: 2013-06-17

Crosschecked: 2013-08-07

Cited: 7

Clicked: 3370

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.9 P.722-732

http://doi.org/10.1631/jzus.CIIP1301


Primal least squares twin support vector regression


Author(s):  Hua-juan Huang, Shi-fei Ding, Zhong-zhi Shi

Affiliation(s):  School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; more

Corresponding email(s):   dingsf@cumt.edu.cn

Key Words:  Twin support vector regression, Least squares method, Primal space, Stock prediction


Hua-juan Huang, Shi-fei Ding, Zhong-zhi Shi. Primal least squares twin support vector regression[J]. Journal of Zhejiang University Science C, 2013, 14(9): 722-732.

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Abstract: 
The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space. However, this solution is affected by time and memory constraints when dealing with large datasets. In this paper, we present a least squares version for TSVR in the primal space, termed primal least squares TSVR (PLSTSVR). By introducing the least squares method, the inequality constraints of TSVR are transformed into equality constraints. Furthermore, we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space; thus, we need only to solve two systems of linear equations instead of two QPPs. Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational time. We further investigate its validity in predicting the opening price of stock.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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