Normalized Least-Mean-Square Algorithms with Minimax Concave Penalty

Hiroyuki Kaneko, Masahiro Yukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5445-5449
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 2020 May
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 2020 May 42020 May 8

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

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

Keywords

  • adaptive filtering
  • minimax concave penalty
  • normalized least-mean-square algorithm
  • proximal forward-backward splitting
  • soft/firm shrinkage

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kaneko, H., & Yukawa, M. (2020). Normalized Least-Mean-Square Algorithms with Minimax Concave Penalty. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 5445-5449). [9053638] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9053638