Nonlinear Blind Source Separation Using Slow Feature Analysis with Random Features

Abstract

We develop an algorithm RSFA to perform nonlinear blind source separation with temporal constraints. The algorithm is based on slow feature analysis using random Fourier features for shift invariant kernels, followed by a selection procedure to obtain the sought-after signals. This method not only obtains remarkable results in a short computing time, but also excellently handles situations where there are multiple types of mixtures. In kernel methods, since the problem is unsupervised, the need of multiple kernels is ubiquitous. Experiments on music excerpts illustrate the strong performance of our method.