Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization
Daniele Vigliano, Michele Scarpiniti, Raffaele Parisi, Aurelio Uncini · Machine learning for signal processing ... · 2006
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.