Consensus Based Distributed Sparse Bayesian Learning By Fast Marginal Likelihood Maximization

Consensus Based Distributed Sparse Bayesian Learning By Fast Marginal Likelihood Maximization

Manss, C. (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR))
Shutin, Dmitriy (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR))
Leus, G.J.T. (TU Delft Signal Processing Systems)

2020

For swarm systems, distributed processing is of paramount importance, and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learn- ing (SBL) methods rely on the automatic relevance deter- mination (ARD), which involves a computationally complex reweighted l1-norm optimization, or they use loopy belief propagation, which is not guaranteed to converge. Hence, this paper looks into the fast marginal likelihood maximiza- tion (FMLM) method to develop a faster distributed SBL version. The proposed method has a low communication overhead, and can be distributed by simple consensus meth- ods. The performed simulations indicate a better performance compared with the distributed ARD version, yet the same per- formance as the FMLM.

Consensus Algorithms
Distributed Optimization
Sparse Bayesian Learning

http://resolver.tudelft.nl/uuid:f258b510-35a3-4bf1-a57d-815f110d5a23

https://doi.org/10.1109/LSP.2020.3039481

2021-05-19

1070-9908

IEEE Signal Processing Letters, 27, 2119-2123

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 C. Manss, Dmitriy Shutin, G.J.T. Leus