A geometric blind source separation method based on facet component analysis

A geometric blind source separation method based on facet component analysis

Given a set of mixtures, blind source separation attempts to retrieve the source signals without or with very little information of the mixing process. We present a geometric approach for blind separation of nonnegative linear mixtures termed facet component analysis (FCA). The approach is based on facet identification of the underlying cone structure of the data. Earlier works focus on recover...

Independent component analysis and blind source separation

Independent Component Analysis and Blind Source Separation

Blind source separation and independent component analysis

This special issue of Neurocomputing contains a selection of eleven papers about blind source separation and independent component analysis. The papers represent improved and extended versions of contributed papers presented at the ICA 2004 meeting held in Granada, Spain. A total of 15 manuscripts have been suggested for the special issue by the referees of the contributions submitted to the co...

Blind Source Separation and Independent Component Analysis: A Review

Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree stru...