Robust and SparseLP-Norm Support Vector Regression | Zendy

Ya-Fen Ye | Zendy; Chao Ying | Zendy; YuanHai Shao | Zendy; ChunNa Li | Zendy; Yujuan Chen | Zendy

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Robust and SparseL_P-Norm Support Vector Regression

Author(s) -

Ya-Fen Ye,

Chao Ying,

YuanHai Shao,

ChunNa Li,

Yujuan Chen

Publication year - 2017

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2017.p0989

Subject(s) - outlier , robustness (evolution) , feature selection , computer science , support vector machine , norm (philosophy) , robust regression , regression , pattern recognition (psychology) , artificial intelligence , nonlinear system , nonlinear regression , regression analysis , algorithm , mathematics , machine learning , statistics , biochemistry , chemistry , physics , quantum mechanics , political science , law , gene

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

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