Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback, we, based on the tensor-tensor product (t-product)...
Saved in:
| Published in | Neurocomputing (Amsterdam) Vol. 458; pp. 204 - 218 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
11.10.2021
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 1872-8286 |
| DOI | 10.1016/j.neucom.2021.06.020 |
Cover
| Abstract | Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback, we, based on the tensor-tensor product (t-product), propose a new LRTC method-the unified tensor factorization (UTF)-for 3-way tensor completion. We first integrate the tensor factorization (TF) and the tensor nuclear norm (TNN) regularization into a framework that inherits the benefits of both TF and TNN: fast calculation and convex optimization. The conditions under which TF and TNN are equivalent are analyzed. Then, UTF for tensor completion is presented and an efficient iterative updated algorithm based on the alternate direction method of multipliers (ADMM) is used for our UTF optimization, and the solution of the proposed alternate minimization algorithm is also proven to be able to converge to a Karush–Kuhn–Tucker (KKT) point. Finally, numerical experiments on synthetic data completion and image/video inpainting tasks demonstrate the effectiveness of our method over other state-of-the-art tensor completion methods. |
|---|---|
| AbstractList | Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback, we, based on the tensor-tensor product (t-product), propose a new LRTC method-the unified tensor factorization (UTF)-for 3-way tensor completion. We first integrate the tensor factorization (TF) and the tensor nuclear norm (TNN) regularization into a framework that inherits the benefits of both TF and TNN: fast calculation and convex optimization. The conditions under which TF and TNN are equivalent are analyzed. Then, UTF for tensor completion is presented and an efficient iterative updated algorithm based on the alternate direction method of multipliers (ADMM) is used for our UTF optimization, and the solution of the proposed alternate minimization algorithm is also proven to be able to converge to a Karush–Kuhn–Tucker (KKT) point. Finally, numerical experiments on synthetic data completion and image/video inpainting tasks demonstrate the effectiveness of our method over other state-of-the-art tensor completion methods. |
| Author | Xiao, Qingjiang Ma, Yide Shi, Yuqing Du, Shiqiang Cucchiara, Rita |
| Author_xml | – sequence: 1 givenname: Shiqiang surname: Du fullname: Du, Shiqiang email: shiqiangdu@hotmail.com organization: College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730000, China – sequence: 2 givenname: Qingjiang surname: Xiao fullname: Xiao, Qingjiang organization: China National Information Technology Research Institute, Northwest Minzu University, Lanzhou 730030, China – sequence: 3 givenname: Yuqing surname: Shi fullname: Shi, Yuqing organization: College of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China – sequence: 4 givenname: Rita surname: Cucchiara fullname: Cucchiara, Rita organization: Department of Engineering, University of Modena and Reggio Emilia, Modena 41121, Italy – sequence: 5 givenname: Yide surname: Ma fullname: Ma, Yide organization: School of Information Science and Engineering, Lanzhou University, Lanzhou 730030, China |
| BookMark | eNqNkEFPwyAUx4mZidv0G3joF2jl0ZVSDyZm0WmyxIs7E0pBmR000LnMTy-z7uJBPT3Ce78_vN8EjayzCqFLwBlgoFfrzKqtdJuMYAIZphkm-ASNgZUkZYTRERrjihQpyYGcoUkIa4yhBFKNEV9Zo_fGviS9ssH5RAvZO28-RG-cTYRtjg27la0SsTq_SUTXeSfkqwqJjr3W7VIv7NtxNv6la9Uh4RydatEGdfFdp2h1f_c8f0iXT4vH-e0ylTNgfVpRkKXWUANoACZmTYFzrWiT0zJe1oySgjKmQZOyzKs6rxuphdBFKYs6nvIpKobcre3EfifalnfebITfc8D8YImv-WCJHyxxTHm0FLnrgZPeheCV5tL0X7v3Xpj2L3j2A_7nmzcDpqKQd6M8D9IoK1VjvJI9b5z5PeAT9pOceA |
| CitedBy_id | crossref_primary_10_3390_rs15153862 crossref_primary_10_1080_1206212X_2023_2219836 crossref_primary_10_1007_s10915_023_02364_6 crossref_primary_10_1016_j_geothermics_2024_103204 crossref_primary_10_1007_s10915_022_02006_3 crossref_primary_10_1016_j_sigpro_2025_109935 crossref_primary_10_1109_ACCESS_2023_3291744 crossref_primary_10_1016_j_patcog_2022_109169 crossref_primary_10_1016_j_neucom_2021_10_013 crossref_primary_10_1007_s10489_023_04477_9 crossref_primary_10_1007_s10489_023_04538_z crossref_primary_10_1016_j_neucom_2024_127513 crossref_primary_10_1109_TIP_2024_3489272 crossref_primary_10_1007_s11063_023_11260_x crossref_primary_10_1109_TITS_2023_3308938 crossref_primary_10_3390_app15010322 crossref_primary_10_1109_TSP_2024_3504292 |
| Cites_doi | 10.1117/12.2224039 10.1080/10556788.2012.700713 10.1109/TPAMI.2012.39 10.1109/ALLERTON.2009.5394534 10.1109/TSP.2016.2572047 10.1145/2512329 10.1007/s10994-013-5366-3 10.1137/110841229 10.1109/TPAMI.2015.2392756 10.1007/s10208-009-9045-5 10.1109/TIP.2014.2305840 10.1109/TIP.2017.2762595 10.1109/TPAMI.2017.2748590 10.1137/110837711 10.1109/TCYB.2018.2832085 10.1016/j.neucom.2013.11.020 10.1090/S0025-5718-2012-02598-1 10.1088/0266-5611/27/2/025010 10.1007/BF02289464 10.1016/j.knosys.2016.11.013 10.1609/aaai.v30i1.10266 10.1016/j.laa.2015.07.021 10.1007/s11263-018-1086-2 10.1016/j.laa.2010.09.020 10.1016/j.chemolab.2010.08.004 10.1109/TSP.2016.2639466 10.3934/ipi.2015.9.601 10.1109/TCYB.2014.2374695 10.1109/ALLERTON.2017.8262792 10.1109/TNNLS.2018.2796606 10.1007/s10543-013-0455-z 10.1007/s10618-013-0341-y 10.1109/TNNLS.2016.2611525 10.1109/TNNLS.2015.2423694 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.1016/j.neucom.2021.06.020 |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 218 |
| ExternalDocumentID | oai:iris.unimore.it:11380/1248510 10_1016_j_neucom_2021_06_020 S0925231221009243 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD ADTOC UNPAY |
| ID | FETCH-LOGICAL-c418t-961c7ff1b11f118a4d503fe6d3671b1b8625688f1f27739b3bdcfaaf57c5bcfa3 |
| IEDL.DBID | .~1 |
| ISSN | 0925-2312 1872-8286 |
| IngestDate | Sun Oct 26 04:08:13 EDT 2025 Thu Oct 16 04:35:43 EDT 2025 Thu Apr 24 22:56:43 EDT 2025 Fri Feb 23 02:43:48 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Tensor completion Low-rank tensor Tensor nuclear norm Tensor factorization |
| Language | English |
| License | cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c418t-961c7ff1b11f118a4d503fe6d3671b1b8625688f1f27739b3bdcfaaf57c5bcfa3 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://hdl.handle.net/11380/1248510 |
| PageCount | 15 |
| ParticipantIDs | unpaywall_primary_10_1016_j_neucom_2021_06_020 crossref_citationtrail_10_1016_j_neucom_2021_06_020 crossref_primary_10_1016_j_neucom_2021_06_020 elsevier_sciencedirect_doi_10_1016_j_neucom_2021_06_020 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-10-11 |
| PublicationDateYYYYMMDD | 2021-10-11 |
| PublicationDate_xml | – month: 10 year: 2021 text: 2021-10-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Hillar, Lim (b0195) 2013; 60 Du, Ma, Ma (b0190) 2017; 118 Shang, Cheng, Liu, Luo, Lin (b0255) 2018; 40 Jing, Su, Jin, Zhang (b0030) 2018; 49 Kernfeld, Kilmer, Aeron (b0235) 2015; 485 J. Liu, P. Musialski, P. Wonka, J. Ye, Tensor completion for estimating missing values in visual data., IEEE transactions on pattern analysis and machine intelligence 35 (1) (2013) 208–220. Ashraphijuo, Wang (b0115) 2017; 18 Liu, Shang, Jiao, Cheng, Cheng (b0210) 2015; 45 Zhang, Ely, Aeron, Hao, Kilmer (b0170) 2014 F. Shang, Y. Liu, J. Cheng, Scalable algorithms for tractable schatten quasi-norm minimization, in: Thirtieth AAAI Conference on Artificial Intelligence, 2016. D. Tao, X. Li, W. Hu, S. Maybank, X. Wu, Supervised tensor learning, in: Fifth IEEE International Conference on Data Mining (ICDM’05), IEEE, 2005, pp. 8–pp. Lu, Peng, Wei (b0240) 2019 Yang, Fang, Li, Zeng (b0215) 2016; 64 M. Ashraphijuo, X. Wang, V. Aggarwal, An approximation of the cp-rank of a partially sampled tensor, in: 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, 2017, pp. 604–611. Chen, Han, Wang, Zhao, Meng, Tang (b0050) 2016 Gandy, Recht, Yamada (b0080) 2011; 27 Landsberg (b0010) 2012; 381 Zhou, Lu, Lin, Zhang (b0150) 2018; 3 Martin, Shafer, LaRue (b0165) 2013; 35 Shen, Wen, Zhang (b0245) 2014; 29 Xie, Tao, Zhang, Liu, Zhang, Qu (b0035) 2018; 126 Hu, Tao, Zhang, Xie, Yang (b0180) 2016; 28 Cabral, De la Torre, Costeira, Bernardino (b0250) 2013 Acar, Dunlavy, Kolda, Mørup (b0205) 2011; 106 Xu, Hao, Yin, Su (b0135) 2015; 9 Mørup (b0045) 2011; 1 Ermiş, Acar, Cemgil (b0065) 2015; 29 X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang, Low-tubal-rank tensor completion using alternating minimization, in: Modeling and Simulation for Defense Systems and Applications XI, Vol. 9848, International Society for Optics and Photonics, 2016, p. 984809. Candès, Recht (b0275) 2008; 9 Zhang, Liu, Aeron, Vetro (b0175) 2016 Zhao, Zhou, Zhang, Cichocki, Amari (b0260) 2016; 27 Ashraphijuo, Aggarwal, Wang (b0120) 2017 Zhao, Zhang, Cichocki (b0130) 2015; 37 Semerci, Hao, Kilmer, Miller (b0230) 2014; 23 Mu, Huang, Wright, Goldfarb (b0200) 2014 Zhang, Aeron (b0185) 2016; 65 Feng, Xu, Yan (b0280) 2013 W. Wang, V. Aggarwal, S. Aeron, Tensor completion by alternating minimization under the tensor train (tt) model, arXiv preprint arXiv:1609.05587. Jain, Oh (b0095) 2014 Han, Wang, Zhao, Meng, Lin, Tang (b0265) 2018; 29 Kressner, Steinlechner, Vandereycken (b0070) 2014; 54 Lu, Feng, Chen, Liu, Lin, Yan (b0020) 2016 Symeonidis, Nanopoulos, Manolopoulos (b0060) 2008 Liu, Musialski, Wonka, Ye (b0015) 2012; 35 C. Lu, J. Feng, W. Liu, Z. Lin, S. Yan, et al., Tensor robust principal component analysis with a new tensor nuclear norm, IEEE transactions on pattern analysis and machine intelligence. R. Tomioka, K. Hayashi, H. Kashima, Estimation of low-rank tensors via convex optimization, arXiv preprint arXiv:1010.0789. Tan, Cheng, Wang, Zhang, Ran (b0225) 2014; 133 Yang, Yuan (b0110) 2013; 82 R.A. Harshman, et al., Foundations of the parafac procedure: Models and conditions for an explanatory multimodal factor analysis. Tucker (b0145) 1966; 31 Y. Xie, J. Liu, Y. Qu, D. Tao, W. Zhang, L. Dai, L. Ma, Robust kernelized multiview self-representation for subspace clustering, IEEE Transactions on Neural Networks and Learning Systems. R.H. Keshavan, A. Montanari, S. Oh, Low-rank matrix completion with noisy observations: a quantitative comparison, in: 47th Annual Allerton Conference on Communication, Control, and Computing, IEEE, 2009, pp. 1216–1222. Kilmer, Martin (b0005) 2011; 435 Kilmer, Braman, Hao, Hoover (b0160) 2013; 34 Karatzoglou, Amatriain, Baltrunas, Oliver (b0055) 2010 Signoretto, Dinh, De Lathauwer, Suykens (b0090) 2014; 94 10.1016/j.neucom.2021.06.020_b0155 Ermiş (10.1016/j.neucom.2021.06.020_b0065) 2015; 29 Signoretto (10.1016/j.neucom.2021.06.020_b0090) 2014; 94 Hu (10.1016/j.neucom.2021.06.020_b0180) 2016; 28 Liu (10.1016/j.neucom.2021.06.020_b0210) 2015; 45 Martin (10.1016/j.neucom.2021.06.020_b0165) 2013; 35 Xu (10.1016/j.neucom.2021.06.020_b0135) 2015; 9 Landsberg (10.1016/j.neucom.2021.06.020_b0010) 2012; 381 Kernfeld (10.1016/j.neucom.2021.06.020_b0235) 2015; 485 Shen (10.1016/j.neucom.2021.06.020_b0245) 2014; 29 10.1016/j.neucom.2021.06.020_b0270 10.1016/j.neucom.2021.06.020_b0075 10.1016/j.neucom.2021.06.020_b0025 10.1016/j.neucom.2021.06.020_b0100 Chen (10.1016/j.neucom.2021.06.020_b0050) 2016 10.1016/j.neucom.2021.06.020_b0105 Candès (10.1016/j.neucom.2021.06.020_b0275) 2008; 9 Zhang (10.1016/j.neucom.2021.06.020_b0170) 2014 Du (10.1016/j.neucom.2021.06.020_b0190) 2017; 118 Kressner (10.1016/j.neucom.2021.06.020_b0070) 2014; 54 Liu (10.1016/j.neucom.2021.06.020_b0015) 2012; 35 Lu (10.1016/j.neucom.2021.06.020_b0240) 2019 10.1016/j.neucom.2021.06.020_b0220 Han (10.1016/j.neucom.2021.06.020_b0265) 2018; 29 10.1016/j.neucom.2021.06.020_b0140 Kilmer (10.1016/j.neucom.2021.06.020_b0005) 2011; 435 Cabral (10.1016/j.neucom.2021.06.020_b0250) 2013 Yang (10.1016/j.neucom.2021.06.020_b0110) 2013; 82 Gandy (10.1016/j.neucom.2021.06.020_b0080) 2011; 27 Semerci (10.1016/j.neucom.2021.06.020_b0230) 2014; 23 Lu (10.1016/j.neucom.2021.06.020_b0020) 2016 Zhao (10.1016/j.neucom.2021.06.020_b0130) 2015; 37 Zhou (10.1016/j.neucom.2021.06.020_b0150) 2018; 3 Feng (10.1016/j.neucom.2021.06.020_b0280) 2013 Ashraphijuo (10.1016/j.neucom.2021.06.020_b0115) 2017; 18 Tan (10.1016/j.neucom.2021.06.020_b0225) 2014; 133 Xie (10.1016/j.neucom.2021.06.020_b0035) 2018; 126 Jing (10.1016/j.neucom.2021.06.020_b0030) 2018; 49 Symeonidis (10.1016/j.neucom.2021.06.020_b0060) 2008 Ashraphijuo (10.1016/j.neucom.2021.06.020_b0120) 2017 10.1016/j.neucom.2021.06.020_b0125 Karatzoglou (10.1016/j.neucom.2021.06.020_b0055) 2010 Zhang (10.1016/j.neucom.2021.06.020_b0175) 2016 Shang (10.1016/j.neucom.2021.06.020_b0255) 2018; 40 Yang (10.1016/j.neucom.2021.06.020_b0215) 2016; 64 Mørup (10.1016/j.neucom.2021.06.020_b0045) 2011; 1 Kilmer (10.1016/j.neucom.2021.06.020_b0160) 2013; 34 Mu (10.1016/j.neucom.2021.06.020_b0200) 2014 Jain (10.1016/j.neucom.2021.06.020_b0095) 2014 Acar (10.1016/j.neucom.2021.06.020_b0205) 2011; 106 Zhao (10.1016/j.neucom.2021.06.020_b0260) 2016; 27 10.1016/j.neucom.2021.06.020_b0040 Tucker (10.1016/j.neucom.2021.06.020_b0145) 1966; 31 Zhang (10.1016/j.neucom.2021.06.020_b0185) 2016; 65 10.1016/j.neucom.2021.06.020_b0085 Hillar (10.1016/j.neucom.2021.06.020_b0195) 2013; 60 |
| References_xml | – volume: 29 start-page: 5380 year: 2018 end-page: 5393 ident: b0265 article-title: A generalized model for robust tensor factorization with noise modeling by mixture of gaussians publication-title: IEEE Trans. Neural Networks Learning Syst. – volume: 118 start-page: 56 year: 2017 end-page: 69 ident: b0190 article-title: Graph regularized compact low rank representation for subspace clustering publication-title: Knowl.-Based Syst. – start-page: 73 year: 2014 end-page: 81 ident: b0200 article-title: Square deal: Lower bounds and improved relaxations for tensor recovery publication-title: International conference on machine learning – start-page: 2434 year: 2016 end-page: 2438 ident: b0175 article-title: An online tensor robust pca algorithm for sequential 2d data publication-title: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: 94 start-page: 303 year: 2014 end-page: 351 ident: b0090 article-title: Learning with tensors: a framework based on convex optimization and spectral regularization publication-title: Mach. Learn. – volume: 82 start-page: 301 year: 2013 end-page: 329 ident: b0110 article-title: Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization publication-title: Math. Comput. – volume: 3 start-page: 1152 year: 2018 end-page: 1163 ident: b0150 article-title: Tensor factorization for low-rank tensor completion publication-title: IEEE Trans. Image Process. – volume: 29 start-page: 239 year: 2014 end-page: 263 ident: b0245 article-title: Augmented lagrangian alternating direction method for matrix separation based on low-rank factorization publication-title: Optim. Methods Softw. – volume: 29 start-page: 203 year: 2015 end-page: 236 ident: b0065 article-title: Link prediction in heterogeneous data via generalized coupled tensor factorization publication-title: Data Min. Knowl. Disc. – start-page: 404 year: 2013 end-page: 412 ident: b0280 article-title: Online robust pca via stochastic optimization publication-title: Adv. Neural Inform. Processing Syst. – reference: C. Lu, J. Feng, W. Liu, Z. Lin, S. Yan, et al., Tensor robust principal component analysis with a new tensor nuclear norm, IEEE transactions on pattern analysis and machine intelligence. – volume: 34 start-page: 148 year: 2013 end-page: 172 ident: b0160 article-title: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging publication-title: SIAM J. Matrix Anal. Appl. – start-page: 5249 year: 2016 end-page: 5257 ident: b0020 article-title: Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – volume: 54 start-page: 447 year: 2014 end-page: 468 ident: b0070 article-title: Low-rank tensor completion by riemannian optimization publication-title: BIT Numer. Math. – volume: 31 start-page: 279 year: 1966 end-page: 311 ident: b0145 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika – volume: 37 start-page: 1751 year: 2015 end-page: 1763 ident: b0130 article-title: Bayesian cp factorization of incomplete tensors with automatic rank determination publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 1 start-page: 24 year: 2011 end-page: 40 ident: b0045 article-title: Applications of tensor (multiway array) factorizations and decompositions in data mining publication-title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery – reference: F. Shang, Y. Liu, J. Cheng, Scalable algorithms for tractable schatten quasi-norm minimization, in: Thirtieth AAAI Conference on Artificial Intelligence, 2016. – volume: 49 start-page: 2385 year: 2018 end-page: 2397 ident: b0030 article-title: High-order temporal correlation model learning for time-series prediction publication-title: IEEE Trans. Cybern. – start-page: 5213 year: 2016 end-page: 5221 ident: b0050 article-title: Robust tensor factorization with unknown noise publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – reference: R.A. Harshman, et al., Foundations of the parafac procedure: Models and conditions for an explanatory multimodal factor analysis. – start-page: 2488 year: 2013 end-page: 2495 ident: b0250 article-title: Unifying nuclear norm and bilinear factorization approaches for low-rank matrix decomposition publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 35 start-page: 208 year: 2012 end-page: 220 ident: b0015 article-title: Tensor completion for estimating missing values in visual data publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 27 year: 2011 ident: b0080 article-title: Tensor completion and low-n-rank tensor recovery via convex optimization publication-title: Inverse Prob. – volume: 60 start-page: 45 year: 2013 ident: b0195 article-title: Most tensor problems are np-hard publication-title: J. ACM (JACM) – volume: 435 start-page: 641 year: 2011 end-page: 658 ident: b0005 article-title: Factorization strategies for third-order tensors publication-title: Linear Algebra Appl. – volume: 485 start-page: 545 year: 2015 end-page: 570 ident: b0235 article-title: Tensor–tensor products with invertible linear transforms publication-title: Linear Algebra Appl. – volume: 126 start-page: 1157 year: 2018 end-page: 1179 ident: b0035 article-title: On unifying multi-view self-representations for clustering by tensor multi-rank minimization publication-title: Int. J. Comput. Vision – reference: J. Liu, P. Musialski, P. Wonka, J. Ye, Tensor completion for estimating missing values in visual data., IEEE transactions on pattern analysis and machine intelligence 35 (1) (2013) 208–220. – volume: 9 start-page: 601 year: 2015 end-page: 624 ident: b0135 article-title: Parallel matrix factorization for low-rank tensor completion publication-title: Inverse Problems Imaging – volume: 28 start-page: 2961 year: 2016 end-page: 2973 ident: b0180 article-title: The twist tensor nuclear norm for video completion publication-title: IEEE Trans. Neural Networks Learning Syst. – volume: 35 start-page: A474 year: 2013 end-page: A490 ident: b0165 article-title: An order-p tensor factorization with applications in imaging publication-title: SIAM J. Sci. Comput. – reference: X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang, Low-tubal-rank tensor completion using alternating minimization, in: Modeling and Simulation for Defense Systems and Applications XI, Vol. 9848, International Society for Optics and Photonics, 2016, p. 984809. – start-page: 43 year: 2008 end-page: 50 ident: b0060 article-title: Tag recommendations based on tensor dimensionality reduction publication-title: Proceedings of the 2008 ACM conference on Recommender systems – reference: D. Tao, X. Li, W. Hu, S. Maybank, X. Wu, Supervised tensor learning, in: Fifth IEEE International Conference on Data Mining (ICDM’05), IEEE, 2005, pp. 8–pp. – volume: 381 start-page: 3 year: 2012 ident: b0010 article-title: Tensors: geometry and applications publication-title: Representation Theory – start-page: 3842 year: 2014 end-page: 3849 ident: b0170 article-title: Novel methods for multilinear data completion and de-noising based on tensor-svd publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – start-page: 531 year: 2017 end-page: 535 ident: b0120 article-title: A characterization of sampling patterns for low-tucker-rank tensor completion problem publication-title: 2017 IEEE International Symposium on Information Theory (ISIT) – volume: 45 start-page: 2437 year: 2015 end-page: 2448 ident: b0210 article-title: Trace norm regularized candecomp/parafac decomposition with missing data publication-title: IEEE Trans. Cybern. – reference: W. Wang, V. Aggarwal, S. Aeron, Tensor completion by alternating minimization under the tensor train (tt) model, arXiv preprint arXiv:1609.05587. – volume: 9 start-page: 717 year: 2008 end-page: 772 ident: b0275 article-title: Exact matrix completion via convex optimization publication-title: Found. Comput. Math. – reference: Y. Xie, J. Liu, Y. Qu, D. Tao, W. Zhang, L. Dai, L. Ma, Robust kernelized multiview self-representation for subspace clustering, IEEE Transactions on Neural Networks and Learning Systems. – reference: R.H. Keshavan, A. Montanari, S. Oh, Low-rank matrix completion with noisy observations: a quantitative comparison, in: 47th Annual Allerton Conference on Communication, Control, and Computing, IEEE, 2009, pp. 1216–1222. – start-page: 79 year: 2010 end-page: 86 ident: b0055 article-title: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering publication-title: Proceedings of the fourth ACM conference on Recommender systems – volume: 27 start-page: 736 year: 2016 end-page: 748 ident: b0260 article-title: Bayesian robust tensor factorization for incomplete multiway data publication-title: IEEE Trans. Neural Networks Learning Syst. – volume: 18 start-page: 2116 year: 2017 end-page: 2145 ident: b0115 article-title: Fundamental conditions for low-cp-rank tensor completion publication-title: J. Mach. Learning Res. – volume: 40 start-page: 2066 year: 2018 end-page: 2080 ident: b0255 article-title: Bilinear factor matrix norm minimization for robust pca: Algorithms and applications publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 133 start-page: 161 year: 2014 end-page: 169 ident: b0225 article-title: Tensor completion via a multi-linear low-n-rank factorization model publication-title: Neurocomputing – reference: M. Ashraphijuo, X. Wang, V. Aggarwal, An approximation of the cp-rank of a partially sampled tensor, in: 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, 2017, pp. 604–611. – volume: 65 start-page: 1511 year: 2016 end-page: 1526 ident: b0185 article-title: Exact tensor completion using t-svd publication-title: IEEE Trans. Signal Process. – start-page: 5996 year: 2019 end-page: 6004 ident: b0240 article-title: Low-rank tensor completion with a new tensor nuclear norm induced by invertible linear transforms publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 1431 year: 2014 end-page: 1439 ident: b0095 article-title: Provable tensor factorization with missing data publication-title: Adv. Neural Inform. Process. Syst. – volume: 64 start-page: 4817 year: 2016 end-page: 4829 ident: b0215 article-title: An iterative reweighted method for tucker decomposition of incomplete tensors publication-title: IEEE Trans. Signal Process. – reference: R. Tomioka, K. Hayashi, H. Kashima, Estimation of low-rank tensors via convex optimization, arXiv preprint arXiv:1010.0789. – volume: 106 start-page: 41 year: 2011 end-page: 56 ident: b0205 article-title: Scalable tensor factorizations for incomplete data publication-title: Chemometrics Intell. Lab. Syst. – volume: 23 start-page: 1678 year: 2014 end-page: 1693 ident: b0230 article-title: Tensor-based formulation and nuclear norm regularization for multienergy computed tomography publication-title: IEEE Trans. Image Process. – ident: 10.1016/j.neucom.2021.06.020_b0105 doi: 10.1117/12.2224039 – volume: 29 start-page: 239 issue: 2 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0245 article-title: Augmented lagrangian alternating direction method for matrix separation based on low-rank factorization publication-title: Optim. Methods Softw. doi: 10.1080/10556788.2012.700713 – ident: 10.1016/j.neucom.2021.06.020_b0155 – volume: 35 start-page: 208 issue: 1 year: 2012 ident: 10.1016/j.neucom.2021.06.020_b0015 article-title: Tensor completion for estimating missing values in visual data publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.39 – ident: 10.1016/j.neucom.2021.06.020_b0270 doi: 10.1109/ALLERTON.2009.5394534 – start-page: 1431 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0095 article-title: Provable tensor factorization with missing data publication-title: Adv. Neural Inform. Process. Syst. – ident: 10.1016/j.neucom.2021.06.020_b0075 doi: 10.1109/TPAMI.2012.39 – volume: 64 start-page: 4817 issue: 18 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0215 article-title: An iterative reweighted method for tucker decomposition of incomplete tensors publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2016.2572047 – start-page: 404 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0280 article-title: Online robust pca via stochastic optimization publication-title: Adv. Neural Inform. Processing Syst. – volume: 60 start-page: 45 issue: 6 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0195 article-title: Most tensor problems are np-hard publication-title: J. ACM (JACM) doi: 10.1145/2512329 – ident: 10.1016/j.neucom.2021.06.020_b0085 – volume: 94 start-page: 303 issue: 3 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0090 article-title: Learning with tensors: a framework based on convex optimization and spectral regularization publication-title: Mach. Learn. doi: 10.1007/s10994-013-5366-3 – volume: 35 start-page: A474 issue: 1 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0165 article-title: An order-p tensor factorization with applications in imaging publication-title: SIAM J. Sci. Comput. doi: 10.1137/110841229 – start-page: 2488 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0250 article-title: Unifying nuclear norm and bilinear factorization approaches for low-rank matrix decomposition – volume: 37 start-page: 1751 issue: 9 year: 2015 ident: 10.1016/j.neucom.2021.06.020_b0130 article-title: Bayesian cp factorization of incomplete tensors with automatic rank determination publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2392756 – volume: 9 start-page: 717 issue: 6 year: 2008 ident: 10.1016/j.neucom.2021.06.020_b0275 article-title: Exact matrix completion via convex optimization publication-title: Found. Comput. Math. doi: 10.1007/s10208-009-9045-5 – volume: 23 start-page: 1678 issue: 4 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0230 article-title: Tensor-based formulation and nuclear norm regularization for multienergy computed tomography publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2014.2305840 – volume: 3 start-page: 1152 issue: 27 year: 2018 ident: 10.1016/j.neucom.2021.06.020_b0150 article-title: Tensor factorization for low-rank tensor completion publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2762595 – ident: 10.1016/j.neucom.2021.06.020_b0100 – volume: 40 start-page: 2066 issue: 9 year: 2018 ident: 10.1016/j.neucom.2021.06.020_b0255 article-title: Bilinear factor matrix norm minimization for robust pca: Algorithms and applications publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2748590 – start-page: 5249 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0020 article-title: Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization – volume: 34 start-page: 148 issue: 1 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0160 article-title: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging publication-title: SIAM J. Matrix Anal. Appl. doi: 10.1137/110837711 – volume: 381 start-page: 3 issue: 402 year: 2012 ident: 10.1016/j.neucom.2021.06.020_b0010 article-title: Tensors: geometry and applications publication-title: Representation Theory – volume: 49 start-page: 2385 issue: 6 year: 2018 ident: 10.1016/j.neucom.2021.06.020_b0030 article-title: High-order temporal correlation model learning for time-series prediction publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2832085 – volume: 133 start-page: 161 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0225 article-title: Tensor completion via a multi-linear low-n-rank factorization model publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.11.020 – volume: 82 start-page: 301 issue: 281 year: 2013 ident: 10.1016/j.neucom.2021.06.020_b0110 article-title: Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization publication-title: Math. Comput. doi: 10.1090/S0025-5718-2012-02598-1 – ident: 10.1016/j.neucom.2021.06.020_b0040 – volume: 27 issue: 2 year: 2011 ident: 10.1016/j.neucom.2021.06.020_b0080 article-title: Tensor completion and low-n-rank tensor recovery via convex optimization publication-title: Inverse Prob. doi: 10.1088/0266-5611/27/2/025010 – start-page: 2434 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0175 article-title: An online tensor robust pca algorithm for sequential 2d data – start-page: 73 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0200 article-title: Square deal: Lower bounds and improved relaxations for tensor recovery – volume: 31 start-page: 279 issue: 3 year: 1966 ident: 10.1016/j.neucom.2021.06.020_b0145 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika doi: 10.1007/BF02289464 – volume: 118 start-page: 56 year: 2017 ident: 10.1016/j.neucom.2021.06.020_b0190 article-title: Graph regularized compact low rank representation for subspace clustering publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.11.013 – start-page: 3842 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0170 article-title: Novel methods for multilinear data completion and de-noising based on tensor-svd – start-page: 5213 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0050 article-title: Robust tensor factorization with unknown noise – ident: 10.1016/j.neucom.2021.06.020_b0220 doi: 10.1609/aaai.v30i1.10266 – ident: 10.1016/j.neucom.2021.06.020_b0140 – volume: 485 start-page: 545 year: 2015 ident: 10.1016/j.neucom.2021.06.020_b0235 article-title: Tensor–tensor products with invertible linear transforms publication-title: Linear Algebra Appl. doi: 10.1016/j.laa.2015.07.021 – volume: 126 start-page: 1157 issue: 11 year: 2018 ident: 10.1016/j.neucom.2021.06.020_b0035 article-title: On unifying multi-view self-representations for clustering by tensor multi-rank minimization publication-title: Int. J. Comput. Vision doi: 10.1007/s11263-018-1086-2 – volume: 435 start-page: 641 issue: 3 year: 2011 ident: 10.1016/j.neucom.2021.06.020_b0005 article-title: Factorization strategies for third-order tensors publication-title: Linear Algebra Appl. doi: 10.1016/j.laa.2010.09.020 – start-page: 79 year: 2010 ident: 10.1016/j.neucom.2021.06.020_b0055 article-title: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering – start-page: 531 year: 2017 ident: 10.1016/j.neucom.2021.06.020_b0120 article-title: A characterization of sampling patterns for low-tucker-rank tensor completion problem – start-page: 43 year: 2008 ident: 10.1016/j.neucom.2021.06.020_b0060 article-title: Tag recommendations based on tensor dimensionality reduction – volume: 106 start-page: 41 issue: 1 year: 2011 ident: 10.1016/j.neucom.2021.06.020_b0205 article-title: Scalable tensor factorizations for incomplete data publication-title: Chemometrics Intell. Lab. Syst. doi: 10.1016/j.chemolab.2010.08.004 – volume: 1 start-page: 24 issue: 1 year: 2011 ident: 10.1016/j.neucom.2021.06.020_b0045 article-title: Applications of tensor (multiway array) factorizations and decompositions in data mining publication-title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery – start-page: 5996 year: 2019 ident: 10.1016/j.neucom.2021.06.020_b0240 article-title: Low-rank tensor completion with a new tensor nuclear norm induced by invertible linear transforms – volume: 65 start-page: 1511 issue: 6 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0185 article-title: Exact tensor completion using t-svd publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2016.2639466 – volume: 9 start-page: 601 issue: 2 year: 2015 ident: 10.1016/j.neucom.2021.06.020_b0135 article-title: Parallel matrix factorization for low-rank tensor completion publication-title: Inverse Problems Imaging doi: 10.3934/ipi.2015.9.601 – volume: 45 start-page: 2437 issue: 11 year: 2015 ident: 10.1016/j.neucom.2021.06.020_b0210 article-title: Trace norm regularized candecomp/parafac decomposition with missing data publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2014.2374695 – ident: 10.1016/j.neucom.2021.06.020_b0125 doi: 10.1109/ALLERTON.2017.8262792 – volume: 29 start-page: 5380 issue: 11 year: 2018 ident: 10.1016/j.neucom.2021.06.020_b0265 article-title: A generalized model for robust tensor factorization with noise modeling by mixture of gaussians publication-title: IEEE Trans. Neural Networks Learning Syst. doi: 10.1109/TNNLS.2018.2796606 – volume: 54 start-page: 447 issue: 2 year: 2014 ident: 10.1016/j.neucom.2021.06.020_b0070 article-title: Low-rank tensor completion by riemannian optimization publication-title: BIT Numer. Math. doi: 10.1007/s10543-013-0455-z – volume: 29 start-page: 203 issue: 1 year: 2015 ident: 10.1016/j.neucom.2021.06.020_b0065 article-title: Link prediction in heterogeneous data via generalized coupled tensor factorization publication-title: Data Min. Knowl. Disc. doi: 10.1007/s10618-013-0341-y – volume: 28 start-page: 2961 issue: 12 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0180 article-title: The twist tensor nuclear norm for video completion publication-title: IEEE Trans. Neural Networks Learning Syst. doi: 10.1109/TNNLS.2016.2611525 – volume: 27 start-page: 736 issue: 4 year: 2016 ident: 10.1016/j.neucom.2021.06.020_b0260 article-title: Bayesian robust tensor factorization for incomplete multiway data publication-title: IEEE Trans. Neural Networks Learning Syst. doi: 10.1109/TNNLS.2015.2423694 – ident: 10.1016/j.neucom.2021.06.020_b0025 – volume: 18 start-page: 2116 issue: 1 year: 2017 ident: 10.1016/j.neucom.2021.06.020_b0115 article-title: Fundamental conditions for low-cp-rank tensor completion publication-title: J. Mach. Learning Res. |
| SSID | ssj0017129 |
| Score | 2.4597018 |
| Snippet | Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly... |
| SourceID | unpaywall crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 204 |
| SubjectTerms | Low-rank tensor Tensor completion Tensor factorization Tensor nuclear norm |
| SummonAdditionalLinks | – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA9jO-jF-Ynzixy8ZibNkrTHoY4hODw4mKfSZMlBRzfmxtC_3pe2GaLi9NSmzUfhpbzfI-_3ewhdUmGUVwkn1GlDOkpTEktHiUikhpblCfNs5PuB7A87dyMxqqFQ8O6LvABjPKZXzMtueRpVQ8IUtI4aw8FD96lQ0YsEAYRSnGnGKipI0YEgV2Rx5Xbps0IgxGeFTqev6_2zA9pa5rPsbZVNJp8cTK-JbgJNp8wreWkvF7pt3r-rNv727btopwKYuFvuiD1Us_k-aobiDbj6lw9QCmiz4Dhhn8M-neOy8k5Fy8SwQniRe8njDK4Ab3HQILevGOAunkxXxJd9D32LDHXrZzhEw97t43WfVNUWiOmweEESyYxyjmnGHEQdWWcsKHdWjrlU8FBD6CNkHDvmIqV4orkeG5dlTigjNNzxI1TPp7k9RhggmolFxl0EMW_iqM6sjaiVViobM8VaiAcTpKaSIvcVMSZpyDl7TkvDpd5wqU-9i2gLkfWoWSnFsaG_CtZNKzhRwoQUvMWGke31ZvjTUif_HXCKtn3LO0HGzlB9MV_ac0A3C31R7e4PkcD3Vw priority: 102 providerName: Unpaywall |
| Title | Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion |
| URI | https://dx.doi.org/10.1016/j.neucom.2021.06.020 http://hdl.handle.net/11380/1248510 |
| UnpaywallVersion | submittedVersion |
| Volume | 458 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Complete Freedom Collection [SCCMFC] customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: ACRLP dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIKHN dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1872-8286 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AKRWK dateStart: 19930201 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6iB734Ft_k4DVukjZJe1xEWRUXQRf0VJJuAsrSXXQX8eJvd6ZtFgVB8ZRHk6ZM0swMfPMNISdclQZZwhkPrmSpcZxlOnCmcu2g5ZNcYDTyTV_3BunVg3pYIGcxFgZhle3d39zp9W3d9nRaaXYmT0-dO55L8KKEBKcFqikyfqapwSwGpx9zmIcwQjZ8e1IxHB3D52qMV-VniBmRoOhqFk_M-v2zelqeVRP7_mZHoy_q52KdrLZ2I-02n7ZBFny1SdZiTgba_qJbpAAjsg5doghNH7_QJqFOG21JbTWMDypkMrZQgtVKI7W4f6VgxdLR-I1hNvc4tgaee3zDNhlcnN-f9VibRIGVqcimLNeiNCEIJ0QAZ8KmQ8WT4PUw0QY6HXg0SmdZEEEak-QuccMyWBuUKZWDWrJDFqtx5XcJBcurzJRNggRXNg_cWe8l99pr4zMQ9B5JouyKsmUYx0QXoyJCyZ6LRuIFSrxARJ3ke4TNZ00aho1fxpu4LcW3k1KAEvhl5ul8F_-01P6_lzogK9hCJSfEIVmcvsz8EVgvU3dcH89jstS9vO71oRz0b7uPnxUe8nQ |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6rHvTiW3ybg9e4eTRJe5RFWZ8XFbyFpJuAsnQX3UW8-NudtM2iICiemuZZJmlmBr75BqFjKksdWcIJDa4kmXaU5CpQIgvl4M2LgsVo5Jtb1X_ILh_lYwf1UixMhFW2d39zp9e3dVvTbaXZHT89de9owcGLYhycFihmYg4tZJLr6IGdfMxwHkwz3hDucUli9xQ_V4O8Kj-NoBEOmq6m8Yxpv3_WT4vTamzf3-xw-EX_nK-i5dZwxKfNt62hjq_W0UpKyoDbf3QDGbAi69glHLHpoxfcZNRpwy2xrQapoYpUxhaeYLbixC3uXzGYsXg4eiMxnXvqWyPPfZxhEz2cn933-qTNokDKjOUTUihW6hCYYyyAN2GzgaQieDUQSkOlA5dGqjwPLIDsROGEG5TB2iB1KR2UxBaar0aV30YYTK8yl1YEDr5sEaiz3nPqlVfa5yDoHSSS7EzZUozHTBdDk7Bkz6aRuIkSNxFSx-kOIrNR44Zi45f-Om2L-XZUDGiBX0aezHbxT0vt_nupI7TYv7-5NtcXt1d7aCm2RI3H2D6an7xM_QGYMhN3WB_VTx8S8lk |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA9jO-jF-Ynzixy8ZibNkrTHoY4hODw4mKfSZMlBRzfmxtC_3pe2GaLi9NSmzUfhpbzfI-_3ewhdUmGUVwkn1GlDOkpTEktHiUikhpblCfNs5PuB7A87dyMxqqFQ8O6LvABjPKZXzMtueRpVQ8IUtI4aw8FD96lQ0YsEAYRSnGnGKipI0YEgV2Rx5Xbps0IgxGeFTqev6_2zA9pa5rPsbZVNJp8cTK-JbgJNp8wreWkvF7pt3r-rNv727btopwKYuFvuiD1Us_k-aobiDbj6lw9QCmiz4Dhhn8M-neOy8k5Fy8SwQniRe8njDK4Ab3HQILevGOAunkxXxJd9D32LDHXrZzhEw97t43WfVNUWiOmweEESyYxyjmnGHEQdWWcsKHdWjrlU8FBD6CNkHDvmIqV4orkeG5dlTigjNNzxI1TPp7k9RhggmolFxl0EMW_iqM6sjaiVViobM8VaiAcTpKaSIvcVMSZpyDl7TkvDpd5wqU-9i2gLkfWoWSnFsaG_CtZNKzhRwoQUvMWGke31ZvjTUif_HXCKtn3LO0HGzlB9MV_ac0A3C31R7e4PkcD3Vw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Unifying+tensor+factorization+and+tensor+nuclear+norm+approaches+for+low-rank+tensor+completion&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Du%2C+Shiqiang&rft.au=Xiao%2C+Qingjiang&rft.au=Shi%2C+Yuqing&rft.au=Cucchiara%2C+Rita&rft.date=2021-10-11&rft.issn=0925-2312&rft.volume=458&rft.spage=204&rft.epage=218&rft_id=info:doi/10.1016%2Fj.neucom.2021.06.020&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neucom_2021_06_020 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |