Paper:

# Robust and Sparse *L*_{P}-Norm Support Vector Regression

_{P}

## 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

_{p}-norm, sparse solution, feature selection

A robust and sparse *L _{p}*-norm support vector regression (

*L*-RSVR) is proposed in this paper. The implementation of feature selection in our

_{p}*L*-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics of

_{p}*L*-RSVR are as follows: (i) By using the absolute constraint,

_{p}*L*-RSVR performs robustly against outliers. (ii)

_{p}*L*-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinear

_{p}*L*-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposed

_{p}*L*-RSVR in both feature selection and regression performance as well as its robustness.

_{p}*L*-Norm Support Vector Regression,”

_{P}*J. Adv. Comput. Intell. Intell. Inform.*, Vol.21 No.6, pp. 989-997, 2017.

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