One-unit Learning Rules for Independent Component Analysis

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Aapo Hyvärinen, Erkki Oja


Neural one-unit learning rules for the problem of Independent Com(cid:173) ponent Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a sepa(cid:173) rator that finds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel com(cid:173) putationally efficient fixed-point algorithm is introduced.