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Article: Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors

TitleProbabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors
Authors
Issue Date2017
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
Citation
IEEE Transactions on Signal Processing, 2017, v. 65, p. 663-676 How to Cite?
AbstractTensor canonical polyadic decomposition (CPD), which recovers the latent factor matrices from multidimensional data, is an important tool in signal processing. In many applications, some of the factor matrices are known to have orthogonality structure, and this information can be exploited to improve the accuracy of latent factors recovery. However, existing methods for CPD with orthogonal factors all require the knowledge of tensor rank, which is difficult to acquire, and have no mechanism to handle outliers in measurements. To overcome these disadvantages, in this paper, a novel tensor CPD algorithm based on the probabilistic inference framework is devised. In particular, the problem of tensor CPD with orthogonal factors is interpreted using a probabilistic model, based on which an inference algorithm is proposed that alternatively estimates the factor matrices, recovers the tensor rank and mitigates the outliers. Simulation results using synthetic data and real-world applications are presented to illustrate the excellent performance of the proposed algorithm in terms of accuracy and robustness.
Persistent Identifierhttp://hdl.handle.net/10722/243091
ISSN
2017 Impact Factor: 4.203
2015 SCImago Journal Rankings: 2.004
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCHENG, L-
dc.contributor.authorWu, YC-
dc.contributor.authorPoor, H-
dc.date.accessioned2017-08-25T02:49:54Z-
dc.date.available2017-08-25T02:49:54Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Signal Processing, 2017, v. 65, p. 663-676-
dc.identifier.issn1053-587X-
dc.identifier.urihttp://hdl.handle.net/10722/243091-
dc.description.abstractTensor canonical polyadic decomposition (CPD), which recovers the latent factor matrices from multidimensional data, is an important tool in signal processing. In many applications, some of the factor matrices are known to have orthogonality structure, and this information can be exploited to improve the accuracy of latent factors recovery. However, existing methods for CPD with orthogonal factors all require the knowledge of tensor rank, which is difficult to acquire, and have no mechanism to handle outliers in measurements. To overcome these disadvantages, in this paper, a novel tensor CPD algorithm based on the probabilistic inference framework is devised. In particular, the problem of tensor CPD with orthogonal factors is interpreted using a probabilistic model, based on which an inference algorithm is proposed that alternatively estimates the factor matrices, recovers the tensor rank and mitigates the outliers. Simulation results using synthetic data and real-world applications are presented to illustrate the excellent performance of the proposed algorithm in terms of accuracy and robustness.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78-
dc.relation.ispartofIEEE Transactions on Signal Processing-
dc.rightsIEEE Transactions on Signal Processing. Copyright © IEEE.-
dc.rights©20xx 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.titleProbabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors-
dc.typeArticle-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.identifier.doi10.1109/TSP.2016.2603969-
dc.identifier.hkuros274729-
dc.identifier.volume65-
dc.identifier.spage663-
dc.identifier.epage676-
dc.identifier.isiWOS:000391293800010-
dc.publisher.placeUnited States-

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