Heuristics for Efficient Sparse Blind Source Separation
This work addresses a key optimization bottleneck in analyzing multichannel data for fields like medical imaging and astrophysics, though it appears incremental as it builds on an existing method.
The paper tackles the poor practical separation results of the standard Proximal Alternating Linearized Minimization (PALM) algorithm for Sparse Blind Source Separation by proposing a novel heuristic strategy combined with PALM, demonstrating its relevance on realistic astrophysical data.
Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking the solution of a non-convex penalized matrix factorization problem, its performances largely depend on the optimization strategy. In this context, Proximal Alternating Linearized Minimization (PALM) has become a standard algorithm which, despite its theoretical grounding, generally provides poor practical separation results. In this work, we propose a novel strategy that combines a heuristic approach with PALM. We show its relevance on realistic astrophysical data.