Primal least squares twin support vector regression

Hua-juan HUANG; Shi-fei DING; Zhong-zhi SHI

doi:10.1631/jzus.CIIP1301

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Primal least squares twin support vector regression

Computer & Automation | Updated：2022-12-09

- Primal least squares twin support vector regression
- Primal least squares twin support vector regression
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 9, Pages: 722-732(2013)
- Affiliations：
  
  1.School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
  2.Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
- Author bio：
  
  Shi-fei DING, E-mail: dingsf@cumt.edu.cn
- Funds：
- DOI：10.1631/jzus.CIIP1301
  CLC： TP18
- Received：27 January 2013，
  
  Revised：2013-;8-;7，
  
  Published：2013-09
- Accepted：
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Hua-juan HUANG, Shi-fei DING, Zhong-zhi SHI. Primal least squares twin support vector regression[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 722-732.
DOI：

Hua-juan HUANG, Shi-fei DING, Zhong-zhi SHI. Primal least squares twin support vector regression[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 722-732. DOI： 10.1631/jzus.CIIP1301.

摘要

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.

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references

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