Distributed Splitting-Over-Features Sparse Bayesian Learning with Alternating Direction Method of Multipliers

Conference paper (2018)

Authors

C. Manss Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
Dmitriy Shutin Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2018 C. Manss, Dmitriy Shutin, G.J.T. Leus
DOI: https://doi.org/10.1109/ICASSP.2018.8462229
Multi-agent systems Sparse Bayesian learning ADMM Learning over networks
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

In processing spatially distributed data, multi-agent robotic platforms equipped with sensors and computing capabilities are gaining interest for applications in inhospitable environments. In this work an algorithm for a distributed realization of sparse bayesian learning (SBL) is discussed for learning a static spatial process with the splitting-over-features approach over a network of interconnected agents. The observed process is modeled as a superposition of weighted kernel functions, or features as we call it, centered at the agent's measurement locations. SBL is then used to determine which feature is relevant for representing the spatial process. Using upper bounding convex functions, the SBL parameter estimation is formulated as ℓ1-norm constrained optimization, which is solved distributively using alternating direction method of multipliers (ADMM) and averaged consensus. The performance of the method is demonstrated by processing real magnetic field data collected in a laboratory.