http://arxiv.org/abs/1812.06737
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.
C. Kervazo, J. Bobin and C. Chenot
Tue, 18 Dec 18
46/91
Comments: in Proceedings of iTWIST’18, Paper-ID: 11, Marseille, France, November, 21-23, 2018
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