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We introduce a new information maximization (infomax) approach for the blindsource separation problem. The proposed framework provides aninformation-theoretic perspective for determinant maximization-based structuredmatrix factorization methods such as nonnegative and polytopic matrixfactorization. For this purpose, we use an alternative joint entropy measurebased on the log-determinant of covariance, which we refer to aslog-determinant (LD) entropy. The corresponding (LD) mutual information betweentwo vectors reflects a level of their correlation. We pose the infomax BSScriterion as the maximization of the LD-mutual information between the inputand output of the separator under the constraint that the output vectors lie ina presumed domain set. In contrast to the ICA infomax approach, the proposedinformation maximization approach can separate both dependent and independentsources. Furthermore, we can provide a finite sample guarantee for the perfectseparation condition in the noiseless case.

An Information Maximization Based Blind Source Separation Approach for Dependent and Independent Sources