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38

ICA
2004
Springer

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Independent Slow Feature Analysis and Nonlinear Blind Source Separation

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Independent Slow Feature Analysis and Nonlinear Blind Source Separation

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We present independent slow feature analysis as a new method for nonlinear blind source separation. It circumvents the indeterminacy of nonlinear independent component analysis by constraining the independent components to be as slowly varying as possible. This is achieved by incorporating the principle of slow feature analysis into a nonlinear independent component analysis algorithm based on second-order statistics. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.

Tobias Blaschke, Laurenz Wiskott

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Added	01 Jul 2010
Updated	01 Jul 2010
Type	Conference
Year	2004
Where	ICA
Authors	Tobias Blaschke, Laurenz Wiskott

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