Normalized Least-Mean-Square Algorithms with Minimax Concave Penalty

Hiroyuki Kaneko, Masahiro Yukawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a novel problem formulation for sparsity-aware adaptive filtering based on the nonconvex minimax concave (MC) penalty, aiming to obtain a sparse solution with small estimation bias. We present two algorithms: the first algorithm uses a single firm-shrinkage operation, while the second one uses double soft-shrinkage operations. The twin soft-shrinkage operations compensate each other, promoting sparsity while avoiding a serious increase of biases. The whole cost function is convex in certain parameter settings, while the instantaneous cost function is always nonconvex. Numerical examples show the superiority compared to the existing sparsity-aware adaptive filtering algorithms in system mismatch and sparseness of the solution.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5445-5449
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5
イベント2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
継続期間: 2020 5 42020 5 8

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period20/5/420/5/8

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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