JACIII Vol.21 No.6 pp. 989-997
doi: 10.20965/jaciii.2017.p0989


Robust and Sparse LP-Norm Support Vector Regression

Ya-Fen Ye*, Chao Ying**, Yuan-Hai Shao*, Chun-Na Li*, and Yu-Juan Chen***

*Zhijiang College, Zhejiang University of Technology
182 Zhijiang Road, Hangzhou 310024, China

**Rainbow City Primary School
501 Weiye Road, Hangzhou 310013, China

***School of Data Sciences, Zhejiang University of Finance and Economics
18 Xueyuan Road, Hangzhou 310018, China

December 25, 2016
April 20, 2017
October 20, 2017
support vector regression, Lp-norm, sparse solution, feature selection

A robust and sparse Lp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in our Lp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics of Lp-RSVR are as follows: (i) By using the absolute constraint, Lp-RSVR performs robustly against outliers. (ii) Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinear Lp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposed Lp-RSVR in both feature selection and regression performance as well as its robustness.

Cite this article as:
Y. Ye, C. Ying, Y. Shao, C. Li, and Y. Chen, “Robust and Sparse LP-Norm Support Vector Regression,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.6, pp. 989-997, 2017.
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