Recursive independent component analysis for online blind source separation

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

doi:10.1109/ISCAS.2012.6271896

Recursive independent component analysis for online blind source separation

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

Department of Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	2813-2816
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2012.6271896
Publication status	Published - Sep 28 2012
Event	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of Duration: May 20 2012 → May 23 2012

Conference

Conference	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
Country	Korea, Republic of
City	Seoul
Period	5/20/12 → 5/23/12

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

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AU - Makeig, Scott

AU - Cauwenberghs, Gert

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