Memorias de investigación
Research Publications in journals:
Distributed static linear Gaussian models using consensus
Year:2012
Research Areas
  • Applications for ingineerings and sciences,
  • Mathematical analysis,
  • Electronic technology and of the communications
Information
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.
International
Si
JCR
Si
Title
Neural Networks
ISBN
0893-6080
Impact factor JCR
2,182
Impact info
Volume
Journal number
From page
96
To page
105
Month
SIN MES
Ranking
Participants
  • Autor: Pavle Belanovic . UPM
  • Autor: Sergio Valcarcel Macua UPM
  • Autor: Santiago Zazo Bello UPM
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
  • Departamento: Señales, Sistemas y Radiocomunicaciones