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Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization

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4 Author(s)
Vigliano, D. ; INFOCOM Dept., Univ. degli Studi di Roma "La Sapienza", Rome ; Scarpiniti, M. ; Parisi, R. ; Uncini, A.

This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by the minimization of output mutual information (MMI approach). Nonlinear complex functions involved in the processing are realized by the so called "splitting functions" which work on the real and the imaginary part of the signal respectively. Some experimental results that demonstrate the effectiveness of the proposed method are shown.

Published in:

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Date of Conference:

6-8 Sept. 2006