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Independent Component Analysis Based on First-Order Statistics

Abstract : This communication puts forward a novel method for independent source extraction in instantaneous linear mixtures. The method is based on the conditional mean of the whitened observations and requires some prior knowledge of the positive support of the desired source. A theoretical performance analysis yields the closed-form expression of the asymptotic interference-to-signal ratio achieved by this technique. The analysis includes the effects of inaccuracies in the estimation of the positive support of the desired source in single-step and iterative implementations of the algorithm. Numerical experiments validate the fitness of the asymptotic approximations. As it is based on first-order statistics, the method is extremely cost-effective, which makes it an attractive alternative to second- and higher-order statistical techniques in power-limited scenarios.
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Contributor : Vicente Zarzoso <>
Submitted on : Friday, July 26, 2013 - 2:21:53 PM
Last modification on : Monday, October 12, 2020 - 10:30:32 AM
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Vicente Zarzoso, Rubén Martín-Clemente, Susana Hornillo-Mellado. Independent Component Analysis Based on First-Order Statistics. Signal Processing, Elsevier, 2012, 92 (8), pp.1779-1784. ⟨hal-00848550⟩



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