LASSOの逐次型アルゴリズム

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: 2016/07/01

タイトル(英語): Recursive Algorithm for LASSO

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: スパース推定，L1正則化，逐次アルゴリズム　　Sparse estimation，L1 regularization，Recursive algorithm

要約(英語): In many fields including control systems society, sparse estimations are attracting the most attention. Especially, L1 regularized linear regression is applied to many applications because it is easy to deal with. However, in calculations using all measurement data at once, the more number of measurement becomes, the lager computational costs become. In this paper, we propose a recursive algorithm for the L1 regularized linear regression. In order to derive the proposed recursive algorithm, we introduce upper and lower bounds of a criterion of the L1 regularized linear regression. Moreover, we show that we can solve a minimization problem of the both bounds analytically and recursively, and we use the analytic solutions as an approximate solution of the L1 regularized linear regression. We demonstrate the effectiveness of the proposed method by numerical simulations, in which we use random systems 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.136 No.7 （2016） 特集：平成27年電子・情報・システム部門大会

本誌掲載ページ: 915-922 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/136/7/136_915/_article/-char/ja/