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Faster and better sparse blind source separation through mini-batch optimization

Abstract : Sparse Blind Source Separation (sBSS) plays a key role in scientific domains as different as biomedical imaging, remote sensing or astrophysics, which require the development of increasingly faster and scalable BSS methods without sacrificing the separation performances. To that end, a new distributed sparse BSS algorithm is introduced based on a mini-batch extension of the Generalized Morphological Component Analysis algorithm (GMCA). Precisely, it combines a robust projected alternate least-squares method with mini-batches optimization. The originality further lies in the use of a manifold-based aggregation of asynchronously estimated mixing matrices. Numerical experiments are carried out on realistic spectroscopic spectra , and highlight the ability of the proposed distributed GMCA (dGMCA) to provide very good separation results even when very small mini-batches are used. Quite unexpectedly, it can further outperform the (non-distributed) state-of-the-art methods for highly sparse sources.
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Contributor : Jerome Bobin Connect in order to contact the contributor
Submitted on : Friday, January 3, 2020 - 9:47:56 AM
Last modification on : Wednesday, November 3, 2021 - 9:27:57 AM
Long-term archiving on: : Monday, April 6, 2020 - 6:36:19 PM


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  • HAL Id : hal-02426991, version 1


C Kervazo, T Liaudat, Jerome Bobin. Faster and better sparse blind source separation through mini-batch optimization. 2020. ⟨hal-02426991⟩



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