Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

Bibtex Metadata Paper

Authors

Tobias Blaschke, Laurenz Wiskott

Abstract

In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the origi- nal source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the ob- jectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source sepa- ration. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.