Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: theory and applications
Independent component analysis: theory and applications
A Further Result on the ICA One-Bit-Matching Conjecture
Neural Computation
One-Bit-Matching Conjecture for Independent Component Analysis
Neural Computation
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Estimating source kurtosis directly from observation data for ICA
Signal Processing
Analysis of feasible solutions of the ICA problem under the one-bit-matching condition
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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In solving the problem of noiseless independent component analysis (ICA) in which sources of super- and sub-Gaussian coexist in an unknown manner, one can be lead to a feasible solution using the natural gradient learning algorithm with a kind of switching criterion for the model probability distribution densities to be selected as super- or sub-Gaussians appropriately during the iterations. In this letter, an alternative switching criterion is proposed for the natural gradient learning algorithm to solve the noiseless ICA problem with both super- and sub-Gaussian sources. It is demonstrated by the experiments that this alternative switching criterion works well on the noiseless ICA problem with both super- and sub-Gaussian sources.