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- Publisher Website: 10.1109/SSP49050.2021.9513808
- Scopus: eid_2-s2.0-85113553018
- WOS: WOS:000722246500093
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Conference Paper: Probabilistic Tensor Train Decomposition with Automatic Rank Determination from Noisy Data
Title | Probabilistic Tensor Train Decomposition with Automatic Rank Determination from Noisy Data |
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Authors | |
Keywords | Bayesian Inference Rank Determination Tensor Train Decomposition |
Issue Date | 2021 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269 |
Citation | IEEE Statistical Signal Processing Workshop (SSP) 2021, Virtual Conference, Rio de Janeiro, Brazil, 11-14 July 2021, p. 461-465 How to Cite? |
Abstract | Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many signal processing and machine learning tasks. However, existing TT decomposition methods either require the knowledge of the true TT ranks, or extensive fine-tuning of the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to enable automatic rank determination. Based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Simulation results on synthetic data show the success of the proposed model and algorithm in recovering the ground-truth TT structure from noisy data. |
Persistent Identifier | http://hdl.handle.net/10722/301980 |
ISSN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, L | - |
dc.contributor.author | Cheng, L | - |
dc.contributor.author | Wong, N | - |
dc.contributor.author | Wu, YC | - |
dc.date.accessioned | 2021-08-21T03:29:48Z | - |
dc.date.available | 2021-08-21T03:29:48Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Statistical Signal Processing Workshop (SSP) 2021, Virtual Conference, Rio de Janeiro, Brazil, 11-14 July 2021, p. 461-465 | - |
dc.identifier.issn | 2373-0803 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301980 | - |
dc.description.abstract | Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many signal processing and machine learning tasks. However, existing TT decomposition methods either require the knowledge of the true TT ranks, or extensive fine-tuning of the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to enable automatic rank determination. Based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Simulation results on synthetic data show the success of the proposed model and algorithm in recovering the ground-truth TT structure from noisy data. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269 | - |
dc.relation.ispartof | IEEE Statistical Signal Processing Workshop (SSP) Proceedings | - |
dc.rights | IEEE/SP Workshop on Statistical Signal Processing Proceedings. Copyright © IEEE. | - |
dc.rights | ©2021 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 | Bayesian Inference | - |
dc.subject | Rank Determination | - |
dc.subject | Tensor Train Decomposition | - |
dc.title | Probabilistic Tensor Train Decomposition with Automatic Rank Determination from Noisy Data | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.email | Wu, YC: ycwu@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.identifier.authority | Wu, YC=rp00195 | - |
dc.identifier.doi | 10.1109/SSP49050.2021.9513808 | - |
dc.identifier.scopus | eid_2-s2.0-85113553018 | - |
dc.identifier.hkuros | 324503 | - |
dc.identifier.spage | 461 | - |
dc.identifier.epage | 465 | - |
dc.identifier.isi | WOS:000722246500093 | - |
dc.publisher.place | United States | - |