Using kernel density estimator in nonlinear mixture
Wai Yie Leong, John P. Homer · 2005
Generally, blind separation of sources from their nonlinear mixtures is rather difficult. This nonlinear mapping, constituted by unsupervised linear mixing followed by unknown and invertible nonlinear distortion, is found in many signal processing cases. We propose using a kernel density estimator incorporated within an equivariant gradient algorithm to separate the nonlinear mixed sources. The proposed algorithm is a generalization of natural gradient algorithm and Gram-Charlier series, which is able to adapt to the actual statistical distributions of the sources by estimating the kernel density distribution at the output signals. Experiments are presented to illustrate the performance of our proposed nonlinear blind source separation method.