Automatic shrinkage tuning based on a system-mismatch estimate for sparsity-aware adaptive filtering

Masao Yamagishi, Masahiro Yukawa, Isao Yamada

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

    4 Citations (Scopus)

    Abstract

    Exploiting the sparsity in learning algorithms is a key to achieve excellent performances of adaptive filters. This can be realized by the adaptive proximal forward-backward splitting with carefully chosen parameters. In this paper, we propose an automatic parameter tuning based on a minimization principle of a stochastic approximation of the system-mismatch. The proposed approximation has a Tikhonov-type regularization term, which aims to minimize the disturbance by the update of the adaptive filter and mitigates overfitting to an instantaneous observation. Thanks to these properties, the proposed method realizes adaptive parameter tuning without any user-defined parameters, unlike our previous method that utilizes the user-defined parameter to avoid over-fitting. A numerical example demonstrates the efficacy of the proposed parameter tuning.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4800-4804
    Number of pages5
    ISBN (Electronic)9781509041176
    DOIs
    Publication statusPublished - 2017 Jun 16
    Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
    Duration: 2017 Mar 52017 Mar 9

    Other

    Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
    CountryUnited States
    CityNew Orleans
    Period17/3/517/3/9

    Keywords

    • adaptive proximal forward-backward splitting algorithm
    • automatic parameter tuning
    • Sparsity-aware adaptive filter

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

    Fingerprint Dive into the research topics of 'Automatic shrinkage tuning based on a system-mismatch estimate for sparsity-aware adaptive filtering'. Together they form a unique fingerprint.

  • Cite this

    Yamagishi, M., Yukawa, M., & Yamada, I. (2017). Automatic shrinkage tuning based on a system-mismatch estimate for sparsity-aware adaptive filtering. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 4800-4804). [7953068] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953068