Local stability analysis of flexible independent component analysis algorithm

This paper addresses local stability analysis for the flexible independent component analysis (ICA) algorithm where the generalized Gaussian density model was employed for blind separation of mixtures of sub- and super-Gaussian sources. In the flexible 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 manifold, the flexible ICA algorithm is revisited and some new results about its local stability analysis are presented.

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