Deep Unrolled Architecture for Fast and Accurate Gaussian Independent Vector Analysis
Gaspard Blaise, Clément Cosserat, Émilie Chouzenoux, Jean‐Christophe Pesquet, Tülay Adalı · 2025
Joint blind source separation (JBSS) is an inverse problem arising in engineering, particularly in medical imaging, where multiple signal datasets must be factorized simultaneously. A powerful approach to JBSS is Gaussian independent vector analysis (IVA-G), which models source datasets as independent Gaussian vectors and estimates both precision and demixing matrices. Recently, we introduced PALM-IVA-G, an iterative algorithm derived from the proximal alternating linearized minimization (PALM) framework, to solve IVA-G by minimizing a cost function derived from a maximum-likelihood estimator with provable convergence. However, its computational cost increases with the number of datasets and sources, and it requires careful hyperparameter tuning. To address these challenges, we propose U-PALM-IVA-G, an unrolled version of PALM-IVA-G that leverages deep unfolding to enhance efficiency. Experiments on six synthetic datasets of varying size and complexity demonstrate that U-PALM-IVA-G achieves significant speed improvements and enhanced solution quality compared to PALM-IVA-G.