Deep Unrolled Architecture for Fast and Accurate Gaussian Independent Vector Analysis
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Gaspard Blaise et al. 2025 “Deep Unrolled Architecture for Fast and Accurate Gaussian Independent Vector Analysis,” Paper presented at the SSP 2025 - IEEE Statistical Signal Processing Workshop, Jun 2025, Edimbourg (Ecosse), United Kingdom, May 5, 2025. https://inria.hal.science/hal-05056650v1.
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Abstract
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.