Recursive independent component analysis for online blind source separation

Muhammad Tahir Akhtar, Tzyy Ping Jung, Scott Makeig, Gert Cauwenberghs

Research output: Contribution to conferencePaperpeer-review

38 Citations (Scopus)

Abstract

This study proposes and evaluates a recursive algorithm for incremental estimation of independent components from on-line data. The algorithm offers the convergence properties of batch independent component analysis (ICA) with incremental updates of a form similar to natural gradient (NG) on-line information maximization (Infomax). We employ recursive procedure to arrive at steady state solution given by NG Infomax. Furthermore, we propose a novel procedure to compute corrective updates on the basis of previous estimates. Implementation of this algorithm incurs linear complexity in data size, input dimensions, and number of estimated independent components. Significant gains in convergence rate over on-line natural gradient ICA are demonstrated.

Original languageEnglish
Pages2813-2816
Number of pages4
DOIs
Publication statusPublished - Sep 28 2012
Event2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of
Duration: May 20 2012May 23 2012

Conference

Conference2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
CountryKorea, Republic of
CitySeoul
Period5/20/125/23/12

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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