Differential Hebbian-type learning algorithms for decorrelation and independent component analysis

Differential learning algorithms for decorrelation and independent component analysis (ICA) are presented. It is shown that the proposed differential Hebbian-type learning algorithms are able to successfully decorrelate the non-zero mean-valued data without any preprocessing. Differential learning is also applied for independent component analysis (ICA) so that non-zero mean-valued source signals can be recovered without any preprocessing. It is demonstrated that modified ICA algorithms using differential learning have a superior performance compared to conventional ICA algorithms for the case where the mean values of source signals are non-zero and are changing