Slow feature analysis and decorrelation filtering for separating correlated sources

Minh, Hà Quang; Wiskott, Laurenz

doi:10.1109/iccv.2011.6126327

2011 International Conference on Computer Vision 2011

DOI: 10.1109/iccv.2011.6126327

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Slow feature analysis and decorrelation filtering for separating correlated sources

Hà Quang Minh

Laurenz Wiskott

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Cited by 2 publications

References 19 publications

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Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation

Minh

Wiskott

2013

IEEE Trans. on Image Process.

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We generalize the method of Slow Feature Analysis (SFA) for vector-valued functions of several variables and apply it to the problem of blind source separation, in particular to image separation. It is generally necessary to use multivariate SFA instead of univariate SFA for separating multi-dimensional signals. For the linear case, an exact mathematical analysis is given, which shows in particular that the sources are perfectly separated by SFA if and only if they and their first-order derivatives are uncorrelated. When the sources are correlated, we apply the following technique called Decorrelation Filtering: use a linear filter to decorrelate the sources and their derivatives in the given mixture, then apply the unmixing matrix obtained on the filtered mixtures to the original mixtures. If the filtered sources are perfectly separated by this matrix, so are the original sources. A decorrelation filter can be numerically obtained by solving a nonlinear optimization problem. This technique can also be applied to other linear separation methods, whose output signals are decorrelated, such as ICA. When there are more mixtures than sources, one can determine the actual number of sources by using a regularized version of SFA with decorrelation filtering. Extensive numerical experiments using SFA and ICA with decorrelation filtering, supported by mathematical analysis, demonstrate the potential of our methods for solving problems involving blind source separation.

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Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation

Minh

Wiskott

2013

IEEE Trans. on Image Process.

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Slow Down to Go Better: A Survey on Slow Feature Analysis

Song

Zhao

2024

IEEE Trans. Neural Netw. Learning Syst.

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Slow feature analysis and decorrelation filtering for separating correlated sources

Cited by 2 publications

References 19 publications

Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation

Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation

Slow Down to Go Better: A Survey on Slow Feature Analysis

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