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نویسنده
چکیده
While the FastICA algorithm is a popular procedure for independent component analysis (ICA) and blind source separation, its average convergence behavior has yet to be studied. This paper provides several statistical convergence analyses of the kurtosis-based FastICA algorithm for two-source noiseless mixtures. We derive explicit and approximate expressions for the evolutions of the average value and the p.d.f. of the inter-channel interference (ICI) under arbitrary and uniform priors for the initial separating system vector. Our results support the observation in [1]: this version of the FastICA algorithm reduces the average ICI by 1/3 or 4.77dB at each iteration, independent of the source distributions and initial system state. Simulations verify the analytical results.
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تاریخ انتشار 2008