Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance trade-off. (C) 2012 Elsevier Ltd. All rights reserved.


Published in:
Neural Networks, 34, 96-105
Year:
2012
Publisher:
Oxford, Elsevier
ISSN:
0893-6080
Keywords:
Laboratories:




 Record created 2013-02-27, last modified 2018-03-17


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