The FastICA algorithm is a popular procedure for independent component analysis and blind source separation. In this paper, we analyze the average convergence behavior of the single-unit FastICA algorithm with kurtosis contrast for general m-source noiseless mixtures. We prove that this algorithm causes the average inter-channel interference (ICI) to converge exponentially with a rate of (1/3) or -4.77dB at each iteration, independent of the source mixture kurtoses. Explicit expressions for the average ICI for the three- and four-source mixture cases are also derived, along with an exact expression for the average ICI in a particular situation. Simulations verify the accuracy of the analysis. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Douglas, S. C., Yuan, Z., & Oja, E. (2006). Average convergence behavior of the FastICA algorithm for blind source separation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 790–798). https://doi.org/10.1007/11679363_98
Mendeley helps you to discover research relevant for your work.