Orbit-product representation and correction of gaussian belief propagation

Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov

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

Abstract

We present a new view of Gaussian belief propagation (GaBP) based on a representa- tion of the determinant as a product over or- bits of a graph. We show that the GaBP determinant estimate captures totally backtracking orbits of the graph and consider how to correct this estimate. We show that the missing orbits may be grouped into equiva-lence classes corresponding to backtrackless orbits and the contribution of each equiv- alence class is easily determined from the GaBP solution. Furthermore, we demon- strate that this multiplicative correction fac- tor can be interpreted as the determinant of a backtrackless adjacency matrix of the graph with edge weights based on GaBP. Finally, an efficient method is proposed to compute a truncated correction factor including all backtrackless orbits up to a specified length.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Annual International Conference on Machine Learning, ICML'09
DOIs
StatePublished - 2009
Externally publishedYes
Event26th Annual International Conference on Machine Learning, ICML'09 - Montreal, QC, Canada
Duration: Jun 14 2009Jun 18 2009

Publication series

NameACM International Conference Proceeding Series
Volume382

Conference

Conference26th Annual International Conference on Machine Learning, ICML'09
Country/TerritoryCanada
CityMontreal, QC
Period6/14/096/18/09

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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