An efficient kernel normalized least mean square algorithm with compactly supported kernel

Osamu Toda, Masahiro Yukawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain ℓ1-ball. In the second step, it identifies the exact support by detailed examinations based on an ℓ2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of ℓ2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.

本文言語English
ホスト出版物のタイトル2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3367-3371
ページ数5
ISBN(電子版)9781467369978
DOI
出版ステータスPublished - 2015 8月 4
イベント40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
継続期間: 2014 4月 192014 4月 24

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2015-August
ISSN(印刷版)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
国/地域Australia
CityBrisbane
Period14/4/1914/4/24

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

引用スタイル