著者

Ya-Fen Ye Chao Ying Yue-Xiang Jiang Chun-Na Li

出版者

Fuji Technology Press Ltd.

雑誌

Journal of Advanced Computational Intelligence and Intelligent Informatics (ISSN:13430130)

巻号頁・発行日

vol.21, no.6, pp.1017-1025, 2017-10-20 (Released:2018-11-20)

参考文献数

23

被引用文献数

3

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In this study, we focus on the feature selection problem in regression, and propose a new version of L1 support vector regression (L1-SVR), known as L1-norm least squares support vector regression (L1-LSSVR). The alternating direction method of multipliers (ADMM), a method from the augmented Lagrangian family, is used to solve L1-LSSVR. The sparse solution of L1-LSSVR can realize feature selection effectively. Furthermore, L1-LSSVR is decomposed into a sequence of simpler problems by the ADMM algorithm, resulting in faster training speed. The experimental results demonstrate that L1-LSSVR is not only as effective as L1-SVR, LSSVR, and SVR in both feature selection and regression, but also much faster than L1-SVR and SVR.