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Conference Paper: Distributed Nonnegative Tensor Canonical Polyadic Decomposition With Automatic Rank Determination

TitleDistributed Nonnegative Tensor Canonical Polyadic Decomposition With Automatic Rank Determination
Authors
Issue Date2020
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1000660/all-proceedings
Citation
2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 8-11 June 2020, p. 1-5 How to Cite?
AbstractNonnegative tensor canonical polyadic decomposition (CPD) has found wide-spread applications in various signal processing tasks. However, the implementation of most existing algorithms needs the knowledge of tensor rank, which is difficult to acquire. To address this issue, by interpreting the nonnegative CPD problem using probability density functions (pdfs), the problem is recasted as probabilistic inference with integrated feature of automatic rank determination. Furthermore, to scale the inference algorithm to massive data, its implementation under modern distributed computing architecture is investigated, giving rise to a distributed probabilistic nonnegative tensor CPD algorithm. Numerical studies are presented to show the remarkable performance of the proposed algorithms in terms of accuracy and scalability.
Persistent Identifierhttp://hdl.handle.net/10722/290207
ISSN

 

DC FieldValueLanguage
dc.contributor.authorCheng, L-
dc.contributor.authorTong, X-
dc.contributor.authorWu, YC-
dc.date.accessioned2020-10-22T08:23:32Z-
dc.date.available2020-10-22T08:23:32Z-
dc.date.issued2020-
dc.identifier.citation2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 8-11 June 2020, p. 1-5-
dc.identifier.issn1551-2282-
dc.identifier.urihttp://hdl.handle.net/10722/290207-
dc.description.abstractNonnegative tensor canonical polyadic decomposition (CPD) has found wide-spread applications in various signal processing tasks. However, the implementation of most existing algorithms needs the knowledge of tensor rank, which is difficult to acquire. To address this issue, by interpreting the nonnegative CPD problem using probability density functions (pdfs), the problem is recasted as probabilistic inference with integrated feature of automatic rank determination. Furthermore, to scale the inference algorithm to massive data, its implementation under modern distributed computing architecture is investigated, giving rise to a distributed probabilistic nonnegative tensor CPD algorithm. Numerical studies are presented to show the remarkable performance of the proposed algorithms in terms of accuracy and scalability.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1000660/all-proceedings-
dc.relation.ispartofSensor Array and Multichannel Signal Processing Workshop (SAM)-
dc.rightsSensor Array and Multichannel Signal Processing Workshop (SAM). Copyright © IEEE.-
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.titleDistributed Nonnegative Tensor Canonical Polyadic Decomposition With Automatic Rank Determination-
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/SAM48682.2020.9104278-
dc.identifier.scopuseid_2-s2.0-85092483673-
dc.identifier.hkuros316743-
dc.identifier.spage1-
dc.identifier.epage5-
dc.publisher.placeUnited States-

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