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Conference Paper: Convergence analysis of the information matrix in Gaussian Belief Propagation

TitleConvergence analysis of the information matrix in Gaussian Belief Propagation
Authors
Du, JMa, SWu, YCKar, SMoura, J
Keywordsbelief propagation
graphical model
large-scale networks
Markov random field
Issue Date2017
PublisherInstitute of Electrical and Electronics Engineers. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002
Citation
Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5-9 March 2017, p. 4074 - 4078 How to Cite?
DOI: http://dx.doi.org/10.1109/ICASSP.2017.7952922
AbstractGaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its distance to the unique positive definite limit matrix decreases exponentially fast.
Persistent Identifierhttp://hdl.handle.net/10722/243329
ISSN
1520-6149

 

DC FieldValueLanguage
dc.contributor.authorDu, J-
dc.contributor.authorMa, S-
dc.contributor.authorWu, YC-
dc.contributor.authorKar, S-
dc.contributor.authorMoura, J-
dc.date.accessioned2017-08-25T02:53:24Z-
dc.date.available2017-08-25T02:53:24Z-
dc.date.issued2017-
dc.identifier.citationProceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5-9 March 2017, p. 4074 - 4078-
dc.identifier.issn1520-6149-
dc.identifier.urihttp://hdl.handle.net/10722/243329-
dc.description.abstractGaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its distance to the unique positive definite limit matrix decreases exponentially fast.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002-
dc.relation.ispartofIEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings-
dc.rights©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectbelief propagation-
dc.subjectgraphical model-
dc.subjectlarge-scale networks-
dc.subjectMarkov random field-
dc.titleConvergence analysis of the information matrix in Gaussian Belief Propagation-
dc.typeConference_Paper-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.description.naturepostprint-
dc.identifier.doi10.1109/ICASSP.2017.7952922-
dc.identifier.scopuseid_2-s2.0-85023740162-
dc.identifier.hkuros274941-
dc.identifier.spage4074-
dc.identifier.epage4078-
dc.publisher.placeUnited States-
dc.identifier.issnl1520-6149-

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