Sciweavers

Share
ICA
2004
Springer

Independent Slow Feature Analysis and Nonlinear Blind Source Separation

11 years 3 months ago
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
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
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICA
Authors Tobias Blaschke, Laurenz Wiskott
Comments (0)
books