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Neural Computation, Vol 9, 1457-1482, Copyright © 1997 by The MIT Press


LETTERS

Adaptive On-line Learning Algorithms for Blind Separation-Maximization Entropy and Minimum Mutual Information

Howard Hua Yang and Shun-ichi Amari

There are two major approaches for blind separation: maximum entropy (ME)andminimummutual information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the demixing ma-trix. The MI is the contrast function for blind separation; the entropy is not. To justify the ME, the relation between ME and MMI is first eluci-dated by calculating the first derivative of the entropy and proving that the mean subtraction is necessary in applying the ME and at the solution points determined by the MI, the MEwill not update the demixing matrix in the directions of increasing the cross-talking.


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