Abstract
In this paper we propose an alternative statistical Gaussianity measure whose optimization provides the extraction of one non-gaussian independent component at each stage of an ICA procedure; this measure is based on the Cumulative Density Function (cdf) instead of traditional distribution distances over Probability Density Functions (pdf’s). Additionally, a novel multistage-deflation algorithm is proposed in order to perform ICA in multidimensional scenarios very efficiently; although this approach can be applied to any multistage ICA method, we have developed it to speed up our ICA procedure based on Order Statistics (OS). The algorithm consists on a gradient learning rule plus an orthonormalization projection technique that decreases the vector space dimension progressively 1.
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© 2001 Springer-Verlag Berlin Heidelberg
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Blanco, Y., Zazo, S., Paez-Borrallo, J.M. (2001). Adaptive ICA with Order Statistics in Multidimensional Scenarios. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_93
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DOI: https://doi.org/10.1007/3-540-45723-2_93
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