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A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the orthonormality constraint in a more natural way in the course of performing independent component analysis (ICA) that employs a mutual information-based source-adaptive contrast function. Despite the extensive development of manifold techniques catering to the orthonormality constraint, only a limited number of works have been dedicated to oblique manifold ( OB ) algorithms to intrinsically handle the normality constraint, which has been empirically shown to be superior to other Riemannian and Euclidean approaches. Imposing the normality constraint implicitly, in line with the ICA definition, essentially guarantees a substantial improvement in the solution accuracy, by way of increased degrees of freedom while searching for an optimal unmixing ICA matrix, in contrast with the orthonormality constraint. Designs of the …
Publication date: 
23 Oct 2012

Suviseshamuthu Easter Selvan, Umberto Amato, Kyle A Gallivan, Chunhong Qi, Maria Francesca Carfora, Michele Larobina, Bruno Alfano

Biblio References: 
Volume: 23 Issue: 12 Pages: 1930-1947
IEEE Transactions on Neural Networks and Learning Systems