title: Distributed static linear Gaussian models using consensus.
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.
authors.affiliation: Telecommunications Circuits Laboratory (TCL), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. pavle.belanovic@epfl.ch
authors.forename: Pavle
authors.initials: P
authors.lastname: Belanovic
authors.forename: Sergio
authors.initials: S
authors.lastname: Valcarcel Macua
authors.forename: Santiago
authors.initials: S
authors.lastname: Zazo
mesh_terms: D016014:Linear Models; D016011:Normal Distribution; D010775:Photic Stimulation / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods