Recursive generalized eigendecomposition for independent component analysis

Authors:
Umut Ozertem;Deniz Erdogmus;Tian Lan
Affiliations:
CSEE Department, OGI, Oregon Health & Science University, Portland, OR;CSEE Department, OGI, Oregon Health & Science University, Portland, OR;BME Department, OGI, Oregon Health & Science University, Portland, OR
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

Independent component analysis is an important statistical tool in machine learning, pattern recognition, and signal processing. Most of these applications require on-line learning algorithms. Current on-line ICA algorithms use the stochastic gradient concept, drawbacks of which include difficulties in selecting the step size and generating suboptimal estimates. In this paper a recursive generalized eigendecomposition algorithm is proposed that tracks the optimal solution that one would obtain using all the data observed.