Set-theoretic adaptive filtering based on data-driven sparsification

Masahiro Yukawa, Isao Yamada

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this article, we propose a fast and efficient algorithm named the adaptive parallel Krylov-metric projection algorithm. The proposed algorithm is derived from the variable-metric adaptive projected subgradient method, which has recently been presented as a unified analytic tool for various adaptive filtering algorithms. The proposed algorithm features parallel projection-in a variable-metric sense-onto multiple closed convex sets containing the optimal filter with high probability. The metric is designed based on (i) sparsification by means of a certain data-dependent Krylov subspace and (ii) maximal use of the obtained sparse structure for fast convergence. The numerical examples show the advantages of the proposed algorithm over the existing ones in stationary/nonstationary environments.

Original languageEnglish
Pages (from-to)707-722
Number of pages16
JournalInternational Journal of Adaptive Control and Signal Processing
Issue number8
Publication statusPublished - 2011 Aug
Externally publishedYes


  • set-theoretic adaptive filtering
  • subgradient projection
  • variable-metric projection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Set-theoretic adaptive filtering based on data-driven sparsification'. Together they form a unique fingerprint.

Cite this