Semi-nonnegative independent component analysis: the (3,4)-SENICAexpmethod

Authors:
Julie Coloigner;Laurent Albera;Ahmad Karfoul;Amar Kachenoura;Pierre Comon;Lotfi Senhadji
Affiliations:
Inserm, UMR, Rennes, France and Université de Rennes 1, LTSI, Rennes, France;Inserm, UMR, Rennes, France and Université de Rennes 1, LTSI, Rennes, France;Faculty of Mechanical and Electrical Engineering, University Al-Baath, Homs, Syria;Inserm, UMR, Rennes, France and Université de Rennes 1, LTSI, Rennes, France;CNRS, UMR, Sophia Antipolis, France and I3S, Université de Nice Sophia Antipolis, France;Inserm, UMR, Rennes, France and Université de Rennes 1, LTSI, Rennes, France
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

To solve the Independent Component Analysis (ICA) problem under the constraint of nonnegative mixture, we propose an iterative algorithm, called (3,4)-SENICAexp. This method profits from some interesting properties enjoyed by third and fourth order statistics in the presence of mixed independent processes, imposing the nonnegativity of the mixture by means of an exponential change of variable. This process allows us to obtain an unconstrained problem, optimized using an ELSALS-like procedure. Our approach is tested on synthetic magnetic resonance spectroscopic imaging data and compared to two existing ICA methods, namely SOBI and CoM2.