Adaptive nonlinear estimation based on parallel projection along Affine subspaces in reproducing kernel Hilbert space

Masa Aki Takizawa, Masahiro Yukawa

研究成果: Article査読

25 被引用数 (Scopus)

抄録

We propose a novel algorithm using a reproducing kernel for adaptive nonlinear estimation. The proposed algorithm is based on three ideas: projection-along-subspace, selective update, and parallel projection. The projection-along-subspace yields excellent performances with small dictionary sizes. The selective update effectively reduces the complexity without any serious degradation of performance. The parallel projection leads to fast convergence/tracking accompanied by noise robustness. A convergence analysis in the non-selective-update case is presented by using the adaptive projected subgradient method. Simulation results exemplify the benefits from the three ideas as well as showing the advantages over the state-of-the-art algorithms. The proposed algorithm bridges the quantized kernel least mean square algorithm of Chen and the sparse sequential algorithm of Dodd et al.

本文言語English
論文番号7112637
ページ(範囲)4257-4269
ページ数13
ジャーナルIEEE Transactions on Signal Processing
63
16
DOI
出版ステータスPublished - 2015 8 15

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

フィンガープリント

「Adaptive nonlinear estimation based on parallel projection along Affine subspaces in reproducing kernel Hilbert space」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル