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

Masao Yamagishi, Masahiro Yukawa, Isao Yamada

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages367-370
Number of pages4
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore
Duration: 2010 Dec 142010 Dec 17

Other

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Fingerprint Dive into the research topics of 'Sparse system identification by exponentially weighted adaptive parallel projection and generalized soft-thresholding'. Together they form a unique fingerprint.

  • Cite this

    Yamagishi, M., Yukawa, M., & Yamada, I. (2010). Sparse system identification by exponentially weighted adaptive parallel projection and generalized soft-thresholding. 367-370. Paper presented at 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010, Biopolis, Singapore.