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- Publisher Website: 10.1109/TSP.2020.2975353
- Scopus: eid_2-s2.0-85084192382
- WOS: WOS:000531398900001
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Article: Learning Nonnegative Factors From Tensor Data: Probabilistic Modeling and Inference Algorithm
Title | Learning Nonnegative Factors From Tensor Data: Probabilistic Modeling and Inference Algorithm |
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Authors | |
Keywords | Tensile stress Probabilistic logic Signal processing algorithms Inference algorithms Data mining |
Issue Date | 2020 |
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, 2020, v. 68, p. 1792-1806 How to Cite? |
Abstract | Tensor canonical polyadic decomposition (CPD) with nonnegative factor matrices, which extracts useful latent information from multidimensional data, has found wide-spread applications in various big data analytic tasks. Currently, the implementation of most existing algorithms needs the knowledge of tensor rank. However, this information is practically unknown and difficult to acquire. To address this issue, a probabilistic approach is taken in this paper. Different from previous works, this paper firstly introduces a sparsity-promoting nonnegative Gaussian-gamma prior, based on which a novel probabilistic model for the CPD problem with nonnegative and continuous factors is established. This probabilistic model further enables the derivation of an efficient inference algorithm that accurately learns the nonnegative factors from the tensor data, along with an integrated feature of automatic rank determination. Numerical results using synthetic data and real-world applications are presented to show the remarkable performance of the proposed algorithm. |
Persistent Identifier | http://hdl.handle.net/10722/290169 |
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 | TONG, X | - |
dc.contributor.author | Wang, S | - |
dc.contributor.author | Wu, YC | - |
dc.contributor.author | Poor, HV | - |
dc.date.accessioned | 2020-10-22T08:23:01Z | - |
dc.date.available | 2020-10-22T08:23:01Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Signal Processing, 2020, v. 68, p. 1792-1806 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.uri | http://hdl.handle.net/10722/290169 | - |
dc.description.abstract | Tensor canonical polyadic decomposition (CPD) with nonnegative factor matrices, which extracts useful latent information from multidimensional data, has found wide-spread applications in various big data analytic tasks. Currently, the implementation of most existing algorithms needs the knowledge of tensor rank. However, this information is practically unknown and difficult to acquire. To address this issue, a probabilistic approach is taken in this paper. Different from previous works, this paper firstly introduces a sparsity-promoting nonnegative Gaussian-gamma prior, based on which a novel probabilistic model for the CPD problem with nonnegative and continuous factors is established. This probabilistic model further enables the derivation of an efficient inference algorithm that accurately learns the nonnegative factors from the tensor data, along with an integrated feature of automatic rank determination. Numerical results using synthetic data and real-world applications are presented to show the remarkable performance of the proposed algorithm. | - |
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 | Tensile stress | - |
dc.subject | Probabilistic logic | - |
dc.subject | Signal processing algorithms | - |
dc.subject | Inference algorithms | - |
dc.subject | Data mining | - |
dc.title | Learning Nonnegative Factors From Tensor Data: Probabilistic Modeling and Inference Algorithm | - |
dc.type | Article | - |
dc.identifier.email | Wu, YC: ycwu@eee.hku.hk | - |
dc.identifier.authority | Wu, YC=rp00195 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSP.2020.2975353 | - |
dc.identifier.scopus | eid_2-s2.0-85084192382 | - |
dc.identifier.hkuros | 316739 | - |
dc.identifier.volume | 68 | - |
dc.identifier.spage | 1792 | - |
dc.identifier.epage | 1806 | - |
dc.identifier.isi | WOS:000531398900001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1053-587X | - |