# An Information Maximization Based Blind Source Separation Approach for Dependent and Independent Sources Source: [An Information Maximization Based Blind Source Separation Approach for Dependent and Independent Sources]() Collection index: [llms.txt]() Data updated: 2025-10-25T15:45:26.114Z Authors: Alper T. Erdogan Published: 2022-05-02T10:25:44.000Z ## Abstract We introduce a new information maximization (infomax) approach for the blind source separation problem. The proposed framework provides an information-theoretic perspective for determinant maximization-based structured matrix factorization methods such as nonnegative and polytopic matrix factorization. For this purpose, we use an alternative joint entropy measure based on the log-determinant of covariance, which we refer to as log-determinant (LD) entropy. The corresponding (LD) mutual information between two vectors reflects a level of their correlation. We pose the infomax BSS criterion as the maximization of the LD-mutual information between the input and output of the separator under the constraint that the output vectors lie in a presumed domain set. In contrast to the ICA infomax approach, the proposed information maximization approach can separate both dependent and independent sources. Furthermore, we can provide a finite sample guarantee for the perfect separation condition in the noiseless case. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Sparse Source Separation on Sparse source realizations 30dB input SNR (test)](): Sparse source separation; Sparse source realizations 30dB input SNR (test); SINR: 30. ## Original sources and implementation [Original paper]() [PDF]()