This paper addresses local stability analysis for the exible independent component analysis (ICA) algorithm 6] where the generalized Gaussian density m o d e l w as employed for blind separation of mixtures of sub-and super-Gaussian sources. In the exible ICA algorithm, the shape of nonlinear function in the learning algorithm varies depending on the Gaussian exponent which is properly selected according to the kurtosis of estimated source. In the framework of the natural gradient in Stiefel

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... ifold, the exible ICA algorithm is revisited and some new results about its local stability analysis are presented.