{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T21:15:38Z","timestamp":1783199738825,"version":"3.54.6"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11801418"],"award-info":[{"award-number":["11801418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational and Applied Mathematics"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.cam.2026.117766","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:31:57Z","timestamp":1778340717000},"page":"117766","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Compressed randomized t-CSVD and its applications"],"prefix":"10.1016","volume":"488","author":[{"given":"Hong","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-0013","authenticated-orcid":false,"given":"An-Bao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0001","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.neuroimage.2004.10.043","article-title":"Tensorial extensions of independent component analysis for multisubject fMRI analysis","volume":"25","author":"Beckmann","year":"2005","journal-title":"NeuroImage"},{"key":"10.1016\/j.cam.2026.117766_bib0002","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)","first-page":"1772","article-title":"High order tensor formulation for convolutional sparse coding","author":"Bibi","year":"2017"},{"issue":"8","key":"10.1016\/j.cam.2026.117766_bib0003","first-page":"4355","article-title":"Robust low-tubal-rank tensor recovery from binary measurements","volume":"44","author":"Hou","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.cam.2026.117766_bib0004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2915921","article-title":"Tensors for data mining and data fusion: models, applications, and scalable algorithms","volume":"8","author":"Papalexakis","year":"2016","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"13","key":"10.1016\/j.cam.2026.117766_bib0005","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1109\/TSP.2017.2690524","article-title":"Tensor decomposition for signal processing and machine learning","volume":"65","author":"Sidiropoulos","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"10.1016\/j.cam.2026.117766_bib0006","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"issue":"3","key":"10.1016\/j.cam.2026.117766_bib0007","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02289464","article-title":"Some mathematical notes on three-mode factor analysis","volume":"31","author":"Tucker","year":"1966","journal-title":"Psychometrika"},{"issue":"4","key":"10.1016\/j.cam.2026.117766_bib0008","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1137\/090764189","article-title":"Hierarchical singular value decomposition of tensors","volume":"31","author":"Grasedyck","year":"2010","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"10.1016\/j.cam.2026.117766_bib0009","series-title":"Mathematical problems in image processing: Partial differential equations and the calculus of variations","author":"Aubert","year":"2006"},{"key":"10.1016\/j.cam.2026.117766_bib0010","series-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1363","article-title":"Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting","author":"Jiang","year":"2018"},{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0011","first-page":"1","article-title":"Low-rank tensor completion with spatio-temporal consistency","volume":"28","author":"Wang","year":"2014","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.cam.2026.117766_bib0012","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3603","article-title":"Generalized tensor total variation minimization for visual data recovery","author":"Guo","year":"2015"},{"issue":"6","key":"10.1016\/j.cam.2026.117766_bib0013","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TSP.2016.2639466","article-title":"Exact tensor completion using t-SVD","volume":"65","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"issue":"10","key":"10.1016\/j.cam.2026.117766_bib0014","doi-asserted-by":"crossref","first-page":"10667","DOI":"10.1109\/TCYB.2021.3067676","article-title":"Robust tensor SVD and recovery with rank estimation","volume":"52","author":"Shi","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.cam.2026.117766_bib0015","doi-asserted-by":"crossref","unstructured":"T.X. Jiang, T.Z. Huang, X.L. Zhao, et al., Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm, J. Comput. Appl. Math. 372 (2020) 112680.","DOI":"10.1016\/j.cam.2019.112680"},{"issue":"5","key":"10.1016\/j.cam.2026.117766_bib0016","doi-asserted-by":"crossref","first-page":"A1879","DOI":"10.1137\/16M1093896","article-title":"Recompression of hadamard products of tensors in tucker format","volume":"39","author":"Kressner","year":"2017","journal-title":"SIAM J. Sci. Comput."},{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0017","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1137\/19M1261043","article-title":"Randomized algorithms for low-rank tensor decompositions in the tucker format","volume":"2","author":"Minster","year":"2020","journal-title":"SIAM J. Math. Data Sci."},{"issue":"4","key":"10.1016\/j.cam.2026.117766_bib0018","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1137\/19M1257718","article-title":"Low-rank tucker decomposition of a tensor from streaming data","volume":"2","author":"Sun","year":"2020","journal-title":"SIAM J. Math. Data Sci."},{"issue":"2","key":"10.1016\/j.cam.2026.117766_bib0019","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1137\/17M1112303","article-title":"A practical randomized CP tensor decomposition","volume":"39","author":"Battaglino","year":"2018","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"2","key":"10.1016\/j.cam.2026.117766_bib0020","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/JSTSP.2015.2503260","article-title":"A randomized block sampling approach to canonical polyadic decomposition of large-scale tensors","volume":"10","author":"Vervliet","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"10.1016\/j.cam.2026.117766_bib0021","series-title":"Proceedings of the 36Th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)","first-page":"155","article-title":"Distributed-memory randomized algorithms for sparse tensor CP decomposition","author":"Bharadwaj","year":"2024"},{"key":"10.1016\/j.cam.2026.117766_bib0022","doi-asserted-by":"crossref","unstructured":"S. Ahmadi-Asl, S. Abukhovich, M.G. Asante-Mensah, et al., Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD), IEEE Access 9(2021) 28684\u201328706.","DOI":"10.1109\/ACCESS.2021.3058103"},{"key":"10.1016\/j.cam.2026.117766_bib0023","doi-asserted-by":"crossref","unstructured":"S. Ahmadi-Asl, A randomized algorithm for tensor singular value decomposition using an arbitrary number of passes, Adv. Comput. Math. 98 (2024) 23, Note: Year corrected from 2023 to 2024 based on volume 98.","DOI":"10.1007\/s10915-023-02411-2"},{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0024","article-title":"Randomized algorithms for computing the generalized tensor SVD based on the tensor product","volume":"32","author":"Zhang","year":"2025","journal-title":"Numer. Linear Algebra Appl."},{"issue":"16","key":"10.1016\/j.cam.2026.117766_bib0025","doi-asserted-by":"crossref","first-page":"4409","DOI":"10.1109\/TSP.2018.2853137","article-title":"Subspace-orbit randomized decomposition for low-rank matrix approximations","volume":"66","author":"Kaloorazi","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"issue":"93","key":"10.1016\/j.cam.2026.117766_bib0026","first-page":"1","article-title":"An efficient algorithm for computing the approximate t-URV and its applications","volume":"92","author":"Che","year":"2022","journal-title":"J. Sci. Comput."},{"key":"10.1016\/j.cam.2026.117766_bib0027","doi-asserted-by":"crossref","unstructured":"Y. Zheng, A.B. Xu, Tensor completion via tensor QR decomposition and L2,1-norm minimization, Signal Process. 189(2021) 108240.","DOI":"10.1016\/j.sigpro.2021.108240"},{"issue":"12","key":"10.1016\/j.cam.2026.117766_bib0028","doi-asserted-by":"crossref","first-page":"13257","DOI":"10.1109\/TCSVT.2024.3442295","article-title":"Tensor convolution-like low-rank dictionary for high-dimensional image representation","volume":"34","author":"Xue","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.cam.2026.117766_bib0029","doi-asserted-by":"crossref","unstructured":"F. Aghamohammadi, F. Shakeri, Self representation based methods for tensor completion problem, J. Comput. Appl. Math. 457 (2025) 116297.","DOI":"10.1016\/j.cam.2024.116297"},{"key":"10.1016\/j.cam.2026.117766_bib0030","doi-asserted-by":"crossref","unstructured":"Z.C. Zhang, S.Y. Liu, L.X. Liu, Z.P. Lin, A symmetric ADMM-type algorithm for robust tensor completion problems using a regularized SCAD-Schatten-p model with application in color image and video recovery, J. Comput. Appl. Math. 466 (2025) 116604.","DOI":"10.1016\/j.cam.2025.116604"},{"key":"10.1016\/j.cam.2026.117766_bib0031","doi-asserted-by":"crossref","unstructured":"F.Y. Yang, B. Zheng, R.J. Zhao, Low-rank tensor completion via tensor tri-factorization and sparse transformation, Signal Process. 233(2025) 109935.","DOI":"10.1016\/j.sigpro.2025.109935"},{"key":"10.1016\/j.cam.2026.117766_bib0032","doi-asserted-by":"crossref","unstructured":"T.H. Zhang, J.L. Zhao, S. Fang, Z. Li, Q. Zhang, M.G. Gong, Hyperspectral image restoration via the collaboration of low-rank tensor denoising and completion, Pattern Recognit. 111629(2025).","DOI":"10.1016\/j.patcog.2025.111629"},{"issue":"7","key":"10.1016\/j.cam.2026.117766_bib0033","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1016\/j.patcog.2014.01.007","article-title":"Face recognition by sparse discriminant analysis via joint L2,1-norm minimization","volume":"47","author":"Shi","year":"2014","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.cam.2026.117766_bib0034","doi-asserted-by":"crossref","unstructured":"X. Jin, J. Miao, Q. Wang, et al., Sparse matrix factorization with L2,1 norm for matrix completion, Pattern Recognit. 127(2022) 108655.","DOI":"10.1016\/j.patcog.2022.108655"},{"key":"10.1016\/j.cam.2026.117766_bib0035","doi-asserted-by":"crossref","unstructured":"Y. Kwon, H.S. Oh, TLRR-TF: A fast tensor low-rank representation via tri-factorization, Pattern Recognit. (2025). in press.","DOI":"10.1016\/j.patcog.2025.112762"},{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0036","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","article-title":"Tensor completion for estimating missing values in visual data","volume":"35","author":"Liu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.cam.2026.117766_bib0037","first-page":"9151","article-title":"Tensor ring decomposition with rank minimization on latent space: an efficient approach for tensor completion","volume":"33","author":"Yuan","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"11","key":"10.1016\/j.cam.2026.117766_bib0038","doi-asserted-by":"crossref","first-page":"10794","DOI":"10.1109\/JSEN.2022.3169226","article-title":"Compressed tensor completion: a robust technique for fast and efficient data reconstruction in wireless sensor networks","volume":"22","author":"Sekar","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.cam.2026.117766_bib0039","doi-asserted-by":"crossref","unstructured":"Y. Qiu, G. Zhou, A. Wang, et al., Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion, IEEE Trans. Neural Netw. Learn. Syst. (2024). early access.","DOI":"10.1109\/TNNLS.2024.3373384"},{"issue":"4","key":"10.1016\/j.cam.2026.117766_bib0040","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2019.2891760","article-title":"Tensor robust principal component analysis with a new tensor nuclear norm","volume":"42","author":"Lu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.cam.2026.117766_bib0041","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","article-title":"Factorization strategies for third-order tensors","volume":"435","author":"Kilmer","year":"2011","journal-title":"Linear Algebra Appl."},{"issue":"3","key":"10.1016\/j.cam.2026.117766_bib0042","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TNNLS.2018.2851957","article-title":"A fast and accurate matrix completion method based on QR decomposition and l2,1-norm minimization","volume":"30","author":"Liu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.cam.2026.117766_bib0043","unstructured":"C. Lu, Tensor-Tensor Product Toolbox, Carnegie Mellon University2018, ([Online]. Available: https:\/\/github.com\/canyilu\/tproduct)."}],"container-title":["Journal of Computational and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377042726004085?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377042726004085?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:29:48Z","timestamp":1783196988000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0377042726004085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":43,"alternative-id":["S0377042726004085"],"URL":"https:\/\/doi.org\/10.1016\/j.cam.2026.117766","relation":{},"ISSN":["0377-0427"],"issn-type":[{"value":"0377-0427","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Compressed randomized t-CSVD and its applications","name":"articletitle","label":"Article Title"},{"value":"Journal of Computational and Applied Mathematics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cam.2026.117766","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117766"}}