Distributed static linear Gaussian models using consensus

Belanovic, Pavle and Valcarcel Macua, Sergio and Zazo Bello, Santiago (2012). Distributed static linear Gaussian models using consensus. "Neural Networks", v. 34 ; pp. 96-105. ISSN 0893-6080. https://doi.org/10.1016/j.neunet.2012.07.004.

Description

Title: Distributed static linear Gaussian models using consensus
Author/s:
  • Belanovic, Pavle
  • Valcarcel Macua, Sergio
  • Zazo Bello, Santiago
Item Type: Article
Título de Revista/Publicación: Neural Networks
Date: October 2012
ISSN: 0893-6080
Volume: 34
Subjects:
Freetext Keywords: Principal component analysis; Factor analysis; Distributed systems; Consensus; Gossip
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 tradeoff.

More information

Item ID: 16776
DC Identifier: http://oa.upm.es/16776/
OAI Identifier: oai:oa.upm.es:16776
DOI: 10.1016/j.neunet.2012.07.004
Official URL: http://www.sciencedirect.com/science/article/pii/S0893608012001840
Deposited by: Memoria Investigacion
Deposited on: 10 Aug 2013 09:16
Last Modified: 01 Nov 2014 23:56
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