An efficient data-reusing kernel adaptive filtering algorithm based on Parallel HYperslab Projection along Affine Subspaces

Masa Aki Takizawa, Masahiro Yukawa

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

We propose a novel kernel adaptive filtering algorithm, dubbed Parallel HYperslab Projection along Affine Sub-Spaces (Φ-PASS), which reuses observed data efficiently. We first derive its fully-updating version that projects the current filter onto multiple hyperslabs in parallel along the dictionary subspace. Each hyperslab accommodates one of the data observed up to the present time instant. The algorithm is derived with the adaptive projected subgradient method (APSM) based on which a convergence analysis is presented. We then generalize the algorithm so that only a few coefficients, whose associated dictionary-data are coherent to the datum of each hyperslab, can be updated selectively for low complexity. This is accomplished by performing the hyperslab projections along affine subspaces defined with the selected dictionary-data. Numerical examples show the efficacy of the proposed algorithm.

本文言語English
ホスト出版物のタイトル2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
ページ3557-3561
ページ数5
DOI
出版ステータスPublished - 2013 10 18
外部発表はい
イベント2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
継続期間: 2013 5 262013 5 31

出版物シリーズ

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period13/5/2613/5/31

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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