Stable Robust Regression under Sparse Outlier and Gaussian Noise

Masahiro Yukawa, Kyohei Suzuki, Isao Yamada

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We propose an efficient regression method which is highly robust against outliers and stable even in the severely noisy situations. The robustness here comes from the adoption of the minimax concave loss, while the stability comes from separate treatments of the outlier and noise by an introduction of an auxiliary vector modeling the Gaussian noise. We present a necessary and sufficient condition for convexity of the smooth part of the entire cost under a certain assumption, where a general model is used with its potential use for other applications envisioned. We show that the proposed formulation can be solved via reformulation by the forward-backward-based primal-dual method under the convexity condition. The numerical examples show the remarkable robustness of the proposed estimator under highly noisy situations.

本文言語English
ホスト出版物のタイトル30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ2236-2240
ページ数5
ISBN(電子版)9789082797091
出版ステータスPublished - 2022
イベント30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
継続期間: 2022 8月 292022 9月 2

出版物シリーズ

名前European Signal Processing Conference
2022-August
ISSN(印刷版)2219-5491

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
国/地域Serbia
CityBelgrade
Period22/8/2922/9/2

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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