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- Publisher Website: 10.1109/TSP.2016.2603969
- Scopus: eid_2-s2.0-85027527031
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Article: Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors
Title | Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors |
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Authors | |
Keywords | Multidimensional signal processing orthogonal constraints robust estimation tensor canonical polyadic decomposition |
Issue Date | 2017 |
Publisher | IEEE. 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? |
Abstract | Tensor 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 Identifier | http://hdl.handle.net/10722/243091 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.520 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | CHENG, L | - |
dc.contributor.author | Wu, YC | - |
dc.contributor.author | Poor, H | - |
dc.date.accessioned | 2017-08-25T02:49:54Z | - |
dc.date.available | 2017-08-25T02:49:54Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Signal Processing, 2017, v. 65, p. 663-676 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.uri | http://hdl.handle.net/10722/243091 | - |
dc.description.abstract | Tensor 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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78 | - |
dc.relation.ispartof | IEEE Transactions on Signal Processing | - |
dc.rights | IEEE 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.subject | Multidimensional signal processing | - |
dc.subject | orthogonal constraints | - |
dc.subject | robust estimation | - |
dc.subject | tensor canonical polyadic decomposition | - |
dc.title | Probabilistic Tensor Canonical Polyadic Decomposition With Orthogonal Factors | - |
dc.type | Article | - |
dc.identifier.email | Wu, YC: ycwu@eee.hku.hk | - |
dc.identifier.authority | Wu, YC=rp00195 | - |
dc.identifier.doi | 10.1109/TSP.2016.2603969 | - |
dc.identifier.scopus | eid_2-s2.0-85027527031 | - |
dc.identifier.hkuros | 274729 | - |
dc.identifier.volume | 65 | - |
dc.identifier.spage | 663 | - |
dc.identifier.epage | 676 | - |
dc.identifier.isi | WOS:000391293800010 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1053-587X | - |