Matrix computations (3rd ed.)
A fast fixed-point algorithm for independent component analysis
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Average convergence behavior of the FastICA algorithm for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
On the relationships between power iteration, inverse iteration and FastICA
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Average convergence behavior of the FastICA algorithm for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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The FastICA algorithm is a popular procedure for independent component analysis and blind source separation. Recently, several of its convergence properties have been elucidated, including its average convergence performance and its finite-sample behavior. In this paper, we examine the kurtosis-based algorithm version for two-source mixtures with equal-kurtosis sources, proving that the single-unit FastICA algorithm has dynamical behavior that is identical to the Newton-based Rayleigh Quotient Iteration for finding an eigenvector of a symmetric matrix. We also derive a bound on the average inter-channel interference indicating that the initial convergence rate of FastICA is linear with a rate of (1/3). A simulation indicates its convergence performance.