Dengpan Gao

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Independent component analysis (ICA) has many practical applications in the fields of signal and image processing and several ICA learning algorithms have been constructed via the selection of model probability density functions. However, there is still a lack of deep mathematical theory to validate these ICA algorithms, especially for the general case that(More)
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(More)
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