By combining the batch algorithm with the kernel trick, an improved kernel blind source separation (IKBSS) is presented. The IKBSS has not only a better performance but also a less computational complexity compared to the original kernel blind source separation (KBSS).

In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for no...

This paper discusses several issues related to blind source separation in nonlinear models. Specifically, separability results show that separation in the general case is impossible, however, for specific nonlinear models the problem does have a solution. A specific set of parametric nonlinear mixtures is considered, this set has the Lie group structure. In the parameter set, a group operation ...

While most reported blind source separation methods concern linear mixtures, we here address the nonlinear case. In the first part of this paper, we introduced a general class of nonlinear mixtures which can be inverted using recurrent networks. That part was focused on separating structures themselves and therefore on the non-blind configuration, whereas the current paper addresses the estimat...

In this work, we deal with blind source separation of a class of nonlinear mixtures. The proposed method can be regarded as an adaptation of the solutions developed in [1, 2] to the considered mixing system. Also, we provide a local stability analysis of the employed learning rule, which permits us to establish necessary conditions for an appropriate convergence. The validity of our approach is...