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

arXiv: https://arxiv.org/abs/2205.00794 · HTML: https://mlsanity.com/paper/2205.00794/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2022.

## Built on by (most cited first, 7 of 7)

- [To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review](https://mlsanity.com/paper/2304.09355/) (2023)
- [Self-Supervised Learning with an Information Maximization Criterion](https://mlsanity.com/paper/2209.07999/) (2022)
- [Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources](https://mlsanity.com/paper/2209.12894/) (2022)
- [Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation](https://mlsanity.com/paper/2210.04222/) (2022)
- [Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry](https://mlsanity.com/paper/2306.04810/) (2023)
- [MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization](https://mlsanity.com/paper/2605.01967/) (2026)
- [Normative Networks for Source Separation via Local Plasticity and Dendritic Computation](https://mlsanity.com/paper/2605.19965/) (2026)

## Abstract (arXiv)

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.