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Blind Separation of a Mixture of Uniformly Distributed Source Signals: A Novel Approach

Laboratory for Information Synthesis, RIKEN Brain Science Institute, Institute of Physical and Chemical Research (RIKEN), Wako-shi, Saitama 351-01, Japan

Shun-ichi Amari

Laboratory for Information Synthesis, RIKEN Brain Science Institute, Institute of Physical and Chemical Research (RIKEN), Wako-shi, Saitama 351-01, Japan

Correspondence: The author is on lien from Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India.

A new, efficient algorithm for blind separation of uniformly distributed sources isproposed. The mixing matrix is assumed to be orthogonal by prewhitening the observed signals. The learning rule adaptively estimates the mixing matrix by conceptually rotating a unit hypercube so that all output signal components are contained within or on the hypercube. Under some ideal constraints, it has been theoretically shown that the algorithm is very similar to an ideal O() convergent algorithm, which is much faster than the existing O() convergent algorithms. The algorithm has been generalized to take care ofthe noisy signals by adaptively dilating the hypercube in conjunction with itsrotation.