Recursive Generalized Eigendecomposition for Independent Component Analysis [chapter]

Umut Ozertem, Deniz Erdogmus, Tian Lan
2006 Lecture Notes in Computer Science  
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.
doi:10.1007/11679363_25 fatcat:7u3iiliblrcrtbtct5vwt6d6yy