An alternative switching criterion for independent component analysis (ICA)

In solving the problem of noiseless independent component analysis (ICA) in which sources of superand 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 superor 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 superand sub-Gaussian sources. It is demonstrated by the experiments that this alternative switching criterion works well on the noiseless ICA problem with both superand subGaussian sources. r 2005 Elsevier B.V. All rights reserved.