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

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 ex-tension 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 ma- trices. 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.

Reference: Christophe Kervazo, Tobias Liaudat and Jérôme Bobin.
“Faster and better sparse blind source separation through mini-batch optimization, Digital Signal Processing, Elsevier, 2020.

DSP Elsevier, HAL.

Blind separation of a large number of sparse sources

 

Authors: C. Kervazo, J. Bobin, C. Chenot
Journal: Signal Processing
Year: 2018
Download: Paper


Abstract

Blind Source Separation (BSS) is one of the major tools to analyze multispectral data with applications that range from astronomical to biomedical signal processing. Nevertheless, most BSS methods fail when the number of sources becomes large, typically exceeding a few tens. Since the ability to estimate large number of sources is paramount in a very wide range of applications, we introduce a new algorithm, coined block-Generalized Morphological Component Analysis (bGMCA) to specifically tackle sparse BSS problems when large number of sources need to be estimated. Sparse BSS being a challenging nonconvex inverse problem in nature, the role played by the algorithmic strategy is central, especially when many sources have to be estimated. For that purpose, the bGMCA algorithm builds upon block-coordinate descent with intermediate size blocks. Numerical experiments are provided that show the robustness of the bGMCA algorithm when the sources are numerous. Comparisons have been carried out on realistic simulations of spectroscopic data.

Blind separation of a large number of sparse sources

 

Authors: C. Kervazo, J. Bobin, C. Chenot
Journal: Signal Processing
Year: 2018
Download: Paper


Abstract

Blind Source Separation (BSS) is one of the major tools to analyze multispectral data with applications that range from astronomical to biomedical signal processing. Nevertheless, most BSS methods fail when the number of sources becomes large, typically exceeding a few tens. Since the ability to estimate large number of sources is paramount in a very wide range of applications, we introduce a new algorithm, coined block-Generalized Morphological Component Analysis (bGMCA) to specifically tackle sparse BSS problems when large number of sources need to be estimated. Sparse BSS being a challenging nonconvex inverse problem in nature, the role played by the algorithmic strategy is central, especially when many sources have to be estimated. For that purpose, the bGMCA algorithm builds upon block-coordinate descent with intermediate size blocks. Numerical experiments are provided that show the robustness of the bGMCA algorithm when the sources are numerous. Comparisons have been carried out on realistic simulations of spectroscopic data.