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

作者：

Highlights：

• Lp-norm least squares support vector regression (Lp-LSSVR) is proposed for feature selection in regression.

• Using the absolute constraint and the Lp-norm regularization term, Lp-LSSVR performs robust against outliers.

• Lp-LSSVR ensures the useful features to be selected based on theoretical analysis.

• Lp-LSSVR only solves a series of linear equations, leading to fast training speed.

摘要

•Lp-norm least squares support vector regression (Lp-LSSVR) is proposed for feature selection in regression.•Using the absolute constraint and the Lp-norm regularization term, Lp-LSSVR performs robust against outliers.•Lp-LSSVR ensures the useful features to be selected based on theoretical analysis.•Lp-LSSVR only solves a series of linear equations, leading to fast training speed.

论文关键词：Support vector regression,Feature selection,Lp-norm,Least squares,Robust regression

论文评审过程：Received 28 July 2016, Revised 20 January 2017, Accepted 30 January 2017, Available online 17 February 2017, Version of Record 17 February 2017.

论文官网地址：https://doi.org/10.1016/j.amc.2017.01.062