title: Nonlinear blind source separation using kernels.
abstract: We derive a new method for solving nonlinear blind source separation (BSS) problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the nonlinear domain using the kernel trick originally applied in support vector machines (SVMs). This technique could likewise be applied to other linear covariance-based source separation algorithms. Experiments on realistic nonlinear mixtures of speech signals, gas multisensor data, and visual disparity data illustrate the applicability of our approach.
authors.affiliation: LORIA-CNRS, Vandoeuvre-les-Nancy, France.
authors.forename: D
authors.initials: D
authors.lastname: Martinez
authors.forename: A
authors.initials: A
authors.lastname: Bray