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Conference Paper: Distributed Verification of Belief Precisions Convergence in Gaussian Belief Propagation

TitleDistributed Verification of Belief Precisions Convergence in Gaussian Belief Propagation
Authors
Keywordsbelief propagation
convergence of numerical methods
Gaussian processes
graph theory
signal processing
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000002
Citation
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4-8 May 2020, p. 9115-9119 How to Cite?
AbstractGaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions of Gaussian BP, such as diagonal dominance, walk-summability, and convex decomposition. These classical conditions assume that the convergence conditions for Gaussian BP precisions and means are the same, which has been proved to be unnecessary. Generally, the condition for guaranteeing the convergence of Gaussian BP precisions is looser than that of Gaussian BP means. Moreover, the convergence of Gaussian BP means could be improved by damping when Gaussian BP precisions converge. Therefore, the convergence of Gaussian BP precisions is a prerequisite for guaranteeing the convergence of Gaussian BP means. This paper derives a simple convergence condition for Gaussian BP precisions, which can be verified in a distributed way. Through numerical examples, it is found that there exists scenarios where the new condition is satisfied but the classical conditions are not.
Persistent Identifierhttp://hdl.handle.net/10722/290205
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLi, B-
dc.contributor.authorWu, N-
dc.contributor.authorWu, YC-
dc.date.accessioned2020-10-22T08:23:31Z-
dc.date.available2020-10-22T08:23:31Z-
dc.date.issued2020-
dc.identifier.citation2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4-8 May 2020, p. 9115-9119-
dc.identifier.issn1520-6149-
dc.identifier.urihttp://hdl.handle.net/10722/290205-
dc.description.abstractGaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions of Gaussian BP, such as diagonal dominance, walk-summability, and convex decomposition. These classical conditions assume that the convergence conditions for Gaussian BP precisions and means are the same, which has been proved to be unnecessary. Generally, the condition for guaranteeing the convergence of Gaussian BP precisions is looser than that of Gaussian BP means. Moreover, the convergence of Gaussian BP means could be improved by damping when Gaussian BP precisions converge. Therefore, the convergence of Gaussian BP precisions is a prerequisite for guaranteeing the convergence of Gaussian BP means. This paper derives a simple convergence condition for Gaussian BP precisions, which can be verified in a distributed way. Through numerical examples, it is found that there exists scenarios where the new condition is satisfied but the classical conditions are not.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's 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.rightsIEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2020 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.subjectconvergence of numerical methods-
dc.subjectGaussian processes-
dc.subjectgraph theory-
dc.subjectsignal processing-
dc.titleDistributed Verification of Belief Precisions Convergence in Gaussian Belief Propagation-
dc.typeConference_Paper-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICASSP40776.2020.9054064-
dc.identifier.scopuseid_2-s2.0-85089245541-
dc.identifier.hkuros316740-
dc.identifier.spage9115-
dc.identifier.epage9119-
dc.publisher.placeUnited States-

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