Collaborative training in sensor networks: A graphical model approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs
StatePublished - 2009
EventMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France
Duration: Sep 2 2009Sep 4 2009

Publication series

NameMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Other

OtherMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
CountryFrance
CityGrenoble
Period9/2/099/4/09

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing
  • Education

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