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Abstract : |
Independent Component Analysis (ICA) is a recently developed technique that in many cases characterizes the data in a natural way. The main application area of the linear ICA model is blind source separation. Here, unknown source signals are estimated from their unknown linear mixtures using the strong assumption that the sources are mutually independent. In practice, separation can be achieved by using suitable higher-order statistics or nonlinearities. Various neural approaches have recently been proposed for blind source separation and ICA. In this paper, these approaches and the respective learning algorithms are briefly reviewed, and some extensions of the basic ICA model are discussed. 1., |
