{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T02:10:01Z","timestamp":1726711801559},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1016\/j.knosys.2022.108468","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T23:45:35Z","timestamp":1645573535000},"page":"108468","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":27,"special_numbering":"C","title":["Enhanced tensor low-rank representation for clustering and denoising"],"prefix":"10.1016","volume":"243","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0865-401X","authenticated-orcid":false,"given":"Shiqiang","family":"Du","sequence":"first","affiliation":[]},{"given":"Baokai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guangrong","family":"Shan","sequence":"additional","affiliation":[]},{"given":"Yuqing","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Weilan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.knosys.2022.108468_b1","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","article-title":"Sparse subspace clustering: Algorithm, theory, and applications","volume":"35","author":"Elhamifar","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.knosys.2022.108468_b2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TPAMI.2003.1177153","article-title":"Lambertian reflectance and linear subspaces","volume":"25","author":"Basri","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.knosys.2022.108468_b3","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/BF00129684","article-title":"Shape and motion from image streams under orthography: a factorization method","volume":"9","author":"Tomasi","year":"1992","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.knosys.2022.108468_b4","series-title":"2007 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"A benchmark for the comparison of 3-d motion segmentation algorithms","author":"Tron","year":"2007"},{"key":"10.1016\/j.knosys.2022.108468_b5","unstructured":"G. Liu, Z. Lin, Y. Yu, Robust subspace segmentation by low-rank representation, in: Proceedings of International Conference on Machine Learning, 2010, pp. 663\u2013670."},{"issue":"2","key":"10.1016\/j.knosys.2022.108468_b6","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.knosys.2022.108468_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis?","volume":"58","author":"Cand\u00e8s","year":"2011","journal-title":"J. ACM"},{"issue":"4","key":"10.1016\/j.knosys.2022.108468_b8","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1109\/TCYB.2018.2883566","article-title":"\u21130-Motivated low-rank sparse subspace clustering","volume":"50","author":"Brbi\u0107","year":"2020","journal-title":"IEEE Trans. Cybern."},{"issue":"8","key":"10.1016\/j.knosys.2022.108468_b9","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TCYB.2013.2286106","article-title":"Robust subspace segmentation via low-rank representation","volume":"44","author":"Chen","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2022.108468_b10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.knosys.2016.11.013","article-title":"Graph regularized compact low rank representation for subspace clustering","volume":"118","author":"Du","year":"2017","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"10.1016\/j.knosys.2022.108468_b11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TPAMI.2016.2539946","article-title":"Blessing of dimensionality: Recovering mixture data via dictionary pursuit","volume":"39","author":"Liu","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.knosys.2022.108468_b12","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1109\/TCYB.2018.2811764","article-title":"LRR for subspace segmentation via tractable Schatten-p norm minimization and factorization","volume":"49","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2022.108468_b13","doi-asserted-by":"crossref","unstructured":"Z. Zhang, G. Ely, S. Aeron, N. Hao, M. Kilmer, Novel methods for multilinear data completion and de-noising based on tensor-SVD, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3842\u20133849.","DOI":"10.1109\/CVPR.2014.485"},{"key":"10.1016\/j.knosys.2022.108468_b14","doi-asserted-by":"crossref","unstructured":"C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, S. Yan, Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5249\u20135257.","DOI":"10.1109\/CVPR.2016.567"},{"issue":"1","key":"10.1016\/j.knosys.2022.108468_b15","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TPAMI.2016.2535218","article-title":"Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes","volume":"39","author":"Yang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.knosys.2022.108468_b16","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TCYB.2018.2815559","article-title":"Low-rank 2-D neighborhood preserving projection for enhanced robust image representation","volume":"49","author":"Lu","year":"2019","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"10.1016\/j.knosys.2022.108468_b17","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":"1","key":"10.1016\/j.knosys.2022.108468_b18","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1137\/110837711","article-title":"Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging","volume":"34","author":"Kilmer","year":"2013","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"10.1016\/j.knosys.2022.108468_b19","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.laa.2015.07.021","article-title":"Tensor\u2013tensor products with invertible linear transforms","volume":"485","author":"Kernfeld","year":"2015","journal-title":"Linear Algebra Appl."},{"issue":"5","key":"10.1016\/j.knosys.2022.108468_b20","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1109\/TPAMI.2019.2954874","article-title":"Tensor low-rank representation for data recovery and clustering","volume":"43","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2022.108468_b21","doi-asserted-by":"crossref","unstructured":"D. Meng, F. De\u00a0La\u00a0Torre, Robust matrix factorization with unknown noise, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 1337\u20131344.","DOI":"10.1109\/ICCV.2013.169"},{"issue":"7","key":"10.1016\/j.knosys.2022.108468_b22","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/TPAMI.2017.2732350","article-title":"Robust online matrix factorization for dynamic background subtraction","volume":"40","author":"Yong","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.knosys.2022.108468_b23","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.1109\/TIP.2018.2845123","article-title":"Robust foreground estimation via structured Gaussian scale mixture modeling","volume":"27","author":"Shi","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.knosys.2022.108468_b24","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","article-title":"An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems","volume":"20","author":"Afonso","year":"2011","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.knosys.2022.108468_b25","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2022.108468_b26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neunet.2013.06.013","article-title":"An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion","volume":"48","author":"Liu","year":"2013","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2022.108468_b27","doi-asserted-by":"crossref","unstructured":"G. Liu, S. Yan, Latent low-rank representation for subspace segmentation and feature extraction, in: Proceedings of IEEE International Conference on Computer Vision, 2011, pp. 1615\u20131622.","DOI":"10.1109\/ICCV.2011.6126422"},{"year":"1970","series-title":"Foundations of the PARAFAC procedure: Models and conditions for an\u201c explanatory\u201d multimodal factor analysis","author":"Harshman","key":"10.1016\/j.knosys.2022.108468_b28"},{"issue":"6","key":"10.1016\/j.knosys.2022.108468_b29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1145\/2512329","article-title":"Most tensor problems are NP-hard","volume":"60","author":"Hillar","year":"2013","journal-title":"J. ACM"},{"key":"10.1016\/j.knosys.2022.108468_b30","unstructured":"C. Mu, B. Huang, J. Wright, D. Goldfarb, Square deal: Lower bounds and improved relaxations for tensor recovery, in: International Conference on Machine Learning, 2014, pp. 73\u201381."},{"issue":"3","key":"10.1016\/j.knosys.2022.108468_b31","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":"1","key":"10.1016\/j.knosys.2022.108468_b32","doi-asserted-by":"crossref","first-page":"A474","DOI":"10.1137\/110841229","article-title":"An order-p tensor factorization with applications in imaging","volume":"35","author":"Martin","year":"2013","journal-title":"SIAM J. Sci. Comput."},{"key":"10.1016\/j.knosys.2022.108468_b33","series-title":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"2434","article-title":"An online tensor robust PCA algorithm for sequential 2D data","author":"Zhang","year":"2016"},{"issue":"4","key":"10.1016\/j.knosys.2022.108468_b34","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1109\/TIP.2014.2305840","article-title":"Tensor-based formulation and nuclear norm regularization for multienergy computed tomography","volume":"23","author":"Semerci","year":"2014","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.knosys.2022.108468_b35","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":"2016","journal-title":"IEEE Trans. Signal Process."},{"issue":"4","key":"10.1016\/j.knosys.2022.108468_b36","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":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.knosys.2022.108468_b37","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1109\/TSP.2012.2197748","article-title":"Sparse Bayesian methods for low-rank matrix estimation","volume":"60","author":"Babacan","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.knosys.2022.108468_b38","unstructured":"Q. Zhao, D. Meng, Z. Xu, W. Zuo, L. Zhang, Robust principal component analysis with complex noise, in: International Conference on Machine Learning, 2014, pp. 55\u201363."},{"key":"10.1016\/j.knosys.2022.108468_b39","doi-asserted-by":"crossref","unstructured":"P. Chen, N. Wang, N.L. Zhang, D.-Y. Yeung, Bayesian adaptive matrix factorization with automatic model selection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1284\u20131292.","DOI":"10.1109\/CVPR.2015.7298733"},{"issue":"10","key":"10.1016\/j.knosys.2022.108468_b40","doi-asserted-by":"crossref","first-page":"4677","DOI":"10.1109\/TIP.2016.2593343","article-title":"Robust low-rank matrix factorization under general mixture noise distributions","volume":"25","author":"Cao","year":"2016","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10.1016\/j.knosys.2022.108468_b41","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TPAMI.2012.97","article-title":"Simultaneous video stabilization and moving object detection in turbulence","volume":"35","author":"Oreifej","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2022.108468_b42","doi-asserted-by":"crossref","unstructured":"M. Najafi, L. He, S.Y. Philip, Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization, in: IJCAI, 2019, pp. 3187\u20133194.","DOI":"10.24963\/ijcai.2019\/442"},{"key":"10.1016\/j.knosys.2022.108468_b43","series-title":"Advances in Neural Information Processing Systems","first-page":"612","article-title":"Linearized alternating direction method with adaptive penalty for low-rank representation","author":"Lin","year":"2011"},{"key":"10.1016\/j.knosys.2022.108468_b44","doi-asserted-by":"crossref","unstructured":"C. You, C.-G. Li, D.P. Robinson, R. Vidal, Oracle based active set algorithm for scalable elastic net subspace clustering, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3928\u20133937.","DOI":"10.1109\/CVPR.2016.426"},{"issue":"10","key":"10.1016\/j.knosys.2022.108468_b45","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.1109\/TNNLS.2016.2553155","article-title":"Tensor LRR and sparse coding-based subspace clustering","volume":"27","author":"Fu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"10.1016\/j.knosys.2022.108468_b46","doi-asserted-by":"crossref","first-page":"4022","DOI":"10.1109\/TIP.2014.2343458","article-title":"Enhancing low-rank subspace clustering by manifold regularization","volume":"23","author":"Liu","year":"2014","journal-title":"IEEE Trans. Image Process."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705122001952?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705122001952?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T01:30:34Z","timestamp":1726709434000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705122001952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5]]},"references-count":46,"alternative-id":["S0950705122001952"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2022.108468","relation":{},"ISSN":["0950-7051"],"issn-type":[{"type":"print","value":"0950-7051"}],"subject":[],"published":{"date-parts":[[2022,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhanced tensor low-rank representation for clustering and denoising","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2022.108468","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"108468"}}