Robust Lp-norm least squares support vector regression with feature selection

Ya-Fen Ye, Yuan-Hai Shao, Nai-Yang Deng, Chun-Na Li and Xiang-Yu Hua

Applied Mathematics and Computation, 2017, vol. 305, issue C, 32-52

Abstract: In this paper, we aim a novel algorithm called robust Lp-norm least squares support vector regression (Lp-LSSVR) that is more robust than the traditional least squares support vector regression(LS-SVR). Using the absolute constraint and the Lp-norm regularization term, our Lp-LSSVR performs robust against outliers. Moreover, though the optimization problem is non-convex, the sparse solution of Lp-norm and the lower bonds for nonzero components technique ensure useful features selected by Lp-LSSVR, and it helps to find the local optimum of our Lp-LSSVR. Experimental results show that although Lp-LSSVR is more robust than least squares support vector regression (LS-SVR), and much faster than Lp-norm support vector regression (Lp-SVR) and SVR due to its equality constraint, it is slower than LS-SVR and L1-norm support vector regression (L1-SVR), it is as effective as Lp-SVR, L1-SVR, LS-SVR and SVR in both feature selection and regression.

Keywords: Support vector regression; Feature selection; Lp-norm; Least squares; Robust regression (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:305:y:2017:i:c:p:32-52

DOI: 10.1016/j.amc.2017.01.062

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