Sparse system identification by exponentially weighted adaptive parallel projection and generalized soft-thresholding

Masao Yamagishi, Masahiro Yukawa, Isao Yamada

研究成果: Paper査読

10 被引用数 (Scopus)

抄録

In this paper, we propose a novel online scheme named the adaptive proximal forward-backward splitting method to suppress the sum of'smooth' and'nonsmooth' convex functions, both of which are assumed time-varying. We derive a powerful algorithm for sparse system identification by defining each function as a certain average squared distance ('smooth') and a weighted ℓ1-norm ('nonsmooth'). The smooth term brings an exponentially weighted average of the metric projections of the current estimate onto linear varieties, of which the number grows as new measurements arrive. The presented recursive formula realizes an efficient computation of the average (the exponentially weighted adaptive parallel projection), which contributes the fast and stable convergence. The nonsmooth term, on the other hand, brings the weighted soft-thresholding, contributing the enhancement of the filter sparsity. The weights are adaptively controlled according to the filter coefficients so that the softthresholding gives significant impacts solely to inactive coefficients (coefficients close to zero). The numerical example demonstrates the efficacy of the proposed algorithm.

本文言語English
ページ367-370
ページ数4
出版ステータスPublished - 2010 12月 1
外部発表はい
イベント2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore
継続期間: 2010 12月 142010 12月 17

Other

Other2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010
国/地域Singapore
CityBiopolis
Period10/12/1410/12/17

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム

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