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
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.
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