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

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

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Tobias Blaschke, Laurenz Wiskott


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.