Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Kernel-based nonlinear blind source separation
Neural Computation
Linear and nonlinear ICA based on mutual information: the MISEP method
Signal Processing - Special issue on independent components analysis and beyond
The Journal of Machine Learning Research
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
A generic framework for blind source separation in structurednonlinear models
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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In the linear case, statistical independence is a sufficient criterion for performing blind source separation. In the nonlinear case, however, it leaves an ambiguity in the solutions that has to be resolved by additional criteria. Here we argue that temporal slowness complements statistical independence well and that a combination of the two leads to unique solutions of the nonlinear blind source separation problem. The algorithm we present is a combination of second-order independent component analysis and slow feature analysis and is referred to as independent slow feature analysis. Its performance is demonstrated on nonlinearly mixed music data. We conclude that slowness is indeed a useful complement to statistical independence but that time-delayed second-order moments are only a weak measure of statistical independence.