Semi-Blind Source Separation with Learned Constraints

Blind source separation (BSS) algorithms are unsupervised methods, which are the cornerstone of hyperspectral data analysis by allowing for physically meaningful data decompositions. BSS problems being illposed, the resolution requires efficient regularization schemes to better distinguish between the sources and yield interpretable solutions. For that purpose, we investigate a semi-supervised source separation approach in which we combine a projected alternating least-square algorithm with a learning-based regularization scheme. In this article, we focus on constraining the mixing matrix to belong to a learned manifold by making use of generative models. Altogether, we show that this allows for an innovative BSS algorithm, with improved accuracy, which provides physically interpretable solutions. The proposed method, coined sGMCA, is tested on realistic hyperspectral astrophysical data in challenging scenarios involving strong noise, highly correlated spectra and unbalanced sources. The results highlight the significant benefit of the learned prior to reduce the leakages between the sources, which allows an overall better disentanglement. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

Blind source separation (BSS) algorithms are unsupervised methods that allow for physically meaningful data decompositions. This article proposes a semi-supervised source separation approach that combines a projected alternating least-square algorithm with a learning-based regularization scheme. By constraining the mixing matrix using generative models, the proposed method, sGMCA, achieves improved accuracy and physically interpretable solutions in challenging scenarios.