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NIPS
2004

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

9 years 1 months ago
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source separation. 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.
Tobias Blaschke, Laurenz Wiskott
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Tobias Blaschke, Laurenz Wiskott
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