L₁-Norm Least Squares Support Vector Regression via the Alternating Direction Method of Multipliers

DOI Web Site Web Site 参考文献19件
  • Ye Ya-Fen
    College of Economics, Zhejiang University Zhijiang College, Zhejiang University of Technology
  • Ying Chao
    Rainbow City Primary School
  • Jiang Yue-Xiang
    College of Economics, Zhejiang University
  • Li Chun-Na
    Zhijiang College, Zhejiang University of Technology

書誌事項

タイトル別名
  • L<sub>1</sub>-Norm Least Squares Support Vector Regression via the Alternating Direction Method of Multipliers

この論文をさがす

説明

<p>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.</p>

収録刊行物

参考文献 (19)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ