統計量に基づくL1最小化問題のパラメータ設計手法
統計量に基づくL1最小化問題のパラメータ設計手法
カテゴリ: 論文誌(論文単位)
グループ名: 【C】電子・情報・システム部門
発行日: 2015/11/01
タイトル(英語): Design Method of a Regularization Parameter for L1-Norm Minimization Problem Based on Statistics
著者名: 金田 泰昌(東京都立産業技術研究センター),入月 康晴(東京都立産業技術研究センター)
著者名(英語): Yasuaki Kaneda (Tokyo Metropolitan Industrial Technology Research Institute), Yasuharu Irizuki (Tokyo Metropolitan Industrial Technology Research Institute)
キーワード: L1正則化付き線形回帰,L1正則化付きロジスティック回帰,正則化パラメータ,統計量 L1 regularized linear regression,L1 regularized logistic regression,regularization parameter,statistics
要約(英語): In this paper, we propose a new systematic design method of a regularization parameter for L1-norm minimization problem by using statistics of noise. We consider L1 regularized linear regression andL1 regularized logistic regression as the minimization and we analyze a relationship between their regularization parameters and system parameters. In the case of L1 regularized linear regression, we show that a condition of the regularization parameter is given by LMI parameterized by a covariance matrix of measurement noise. Also in the case of L1 regularized logistic regression, we show that the regularization parameter satisfies LMI described by a variance of a modeling error of a logistic model. Since the proposed design method requires a value of second moment of noise, we estimate the value by a sample covariance matrix. We demonstrate the effectiveness of the proposed method by numerical simulations, in which we use random systems and real data sets to evaluate the proposed method.
本誌: 電気学会論文誌C(電子・情報・システム部門誌) Vol.135 No.11 (2015) 特集:電気関係学会関西連合大会
本誌掲載ページ: 1419-1426 p
原稿種別: 論文/日本語
電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/11/135_1419/_article/-char/ja/
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