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A generalized-statistics variational principle for source separation isformulated by recourse to Tsallis' entropy subjected to the additive dualityand employing constraints described by normal averages. The variationalprinciple is amalgamated with Hopfield-like learning rules resulting in anunsupervised learning model. The update rules are formulated with the aid ofq-deformed calculus. Numerical examples exemplify the efficacy of this model.

Deformed Statistics Free Energy Model for Source Separation using Unsupervised Learning