Empirical Bayes Estimation for <i>L</i>₁ Regularization: A Detailed Analysis in the One-Parameter Lasso Model

• YOSHIDA Tsukasa

  Dept. of Computer Science and Engineering, Toyohashi University of Technology

• WATANABE Kazuho

  Dept. of Computer Science and Engineering, Toyohashi University of Technology

抄録

Lasso regression based on the L₁ regularization is one of the most popular sparse estimation methods. It is often required to set appropriately in advance the regularization parameter that determines the degree of regularization. Although the empirical Bayes approach provides an effective method to estimate the regularization parameter, its solution has yet to be fully investigated in the lasso regression model. In this study, we analyze the empirical Bayes estimator of the one-parameter model of lasso regression and show its uniqueness and its properties. Furthermore, we compare this estimator with that of the variational approximation, and its accuracy is evaluated.

収録刊行物

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A (12), 2184-2191, 2018-12-01

  一般社団法人 電子情報通信学会

キーワード

• lasso regression
• empirical Bayes
• Laplace prior
• local variational approximation

詳細情報

• CRID: 1390282763072368640
• NII論文ID: 130007539073
• DOI: 10.1587/transfun.e101.a.2184
• ISSN: 17451337
• ISSN: 09168508
• Web Site: https://www.jstage.jst.go.jp/article/transfun/E101.A/12/E101.A_2184/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可