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arXiv · 1812.06737

Heuristics for Efficient Sparse Blind Source Separation

Christophe Kervazo·Jerome Bobin·Cecile Chenot
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Abstract

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.

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BibTeXRIS

Christophe Kervazo, Jerome Bobin, Cecile Chenot. 2018-12-17. Heuristics for Efficient Sparse Blind Source Separation. https://arxiv.org/abs/1812.06737

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