Whereas most blind source separation (BSS) and blind mixture identification (BMI)
investigations concern linear mixtures (instantaneous or not), various recent works extended …

In this paper, we review recent advances in blind source separation (BSS) and independent
component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

In this paper, a nonparametric “gradient” of the mutual information is first introduced. It is
used for showing that mutual information has no local minima. Using the introduced …

In the last decade, kernel methods have become established techniques to perform
nonlinear signal processing. Thanks to their foundation in the solid mathematical framework …

In the linear case, statistical independence is a sufficient criterion for performing blind source
separation. In the nonlinear case, however, it leaves an ambiguity in the solutions that has to …

In this paper, we consider the nonlinear Blind Source Separation BSS and independent
component analysis (ICA) problems, and especially uniqueness issues, presenting some …

This letter deals with the resolution of the blind source separation problem using the
independent component analysis method in post-nonlinear mixtures. Using the sole …

This letter proposes a clustering-based approach for solving the underdetermined (ie, fewer
mixtures than sources) postnonlinear blind source separation (PNL BSS) problem when the …

The goal of blind source separation (BSS) lies in recovering the original independent
sources of a mixed random vector without knowing the mixing structure. A key ingredient for …