Paper:

# L_{1}-Norm Least Squares Support Vector Regression via the Alternating Direction Method of Multipliers

## Ya-Fen Ye^{*,**}, Chao Ying^{***}, Yue-Xiang Jiang^{*}, and Chun-Na Li^{**}

^{*}College of Economics, Zhejiang University

Hangzhou 310027, China

^{**}Zhijiang College, Zhejiang University of Technology

Hangzhou 310024, China

^{***}Rainbow City Primary School

Hangzhou 310013, China

_{1}-norm, least squares, feature selection, ADMM

In this study, we focus on the feature selection problem in regression, and propose a new version of L_{1} support vector regression (L_{1}-SVR), known as L_{1}-norm least squares support vector regression (L_{1}-LSSVR). The alternating direction method of multipliers (ADMM), a method from the augmented Lagrangian family, is used to solve L_{1}-LSSVR. The sparse solution of L_{1}-LSSVR can realize feature selection effectively. Furthermore, L_{1}-LSSVR is decomposed into a sequence of simpler problems by the ADMM algorithm, resulting in faster training speed. The experimental results demonstrate that L_{1}-LSSVR is not only as effective as L_{1}-SVR, LSSVR, and SVR in both feature selection and regression, but also much faster than L_{1}-SVR and SVR.

_{1}-Norm Least Squares Support Vector Regression via the Alternating Direction Method of Multipliers,”

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.21, No.6, pp. 1017-1025, 2017.

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