Distributed static linear Gaussian models using consensus

Authors:
Pavle Belanovic;Sergio Valcarcel Macua;Santiago Zazo
Affiliations:
Telecommunications Circuits Laboratory (TCL), ícole Polytechnique Fédérale de Lausanne (EPFL), Switzerland;Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid (UPM), Spain;Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid (UPM), Spain
Venue:
Neural Networks
Year:
2012

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Abstract

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.