Robust jointly-sparse signal recovery based on minimax concave loss function

Kyohei Suzuki, Yukawa Masahiro

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a robust approach to recovering the jointly-sparse signals in the presence of outliers. We formulate the recovering task as a minimization problem involving three terms: (i) the minimax concave (MC) loss function, (ii) the MC penalty function, and (iii) the squared Frobenius norm. The MC-based loss and penalty functions enhance robustness and group sparsity, respectively, while the squared Frobenius norm induces the convexity. The problem is solved, via reformulation, by the primal-dual splitting method, for which the convergence condition is derived. Numerical examples show that the proposed approach enjoys remarkable outlier robustness.

本文言語English
ホスト出版物のタイトル28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ2070-2074
ページ数5
ISBN(電子版)9789082797053
DOI
出版ステータスPublished - 2021 1月 24
イベント28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
継続期間: 2020 8月 242020 8月 28

出版物シリーズ

名前European Signal Processing Conference
2021-January
ISSN(印刷版)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
国/地域Netherlands
CityAmsterdam
Period20/8/2420/8/28

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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