{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T20:26:16Z","timestamp":1725481576898},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976182","62076171","61876157","61976245"],"award-info":[{"award-number":["61976182","62076171","61876157","61976245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s10489-022-04141-8","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T11:11:50Z","timestamp":1664536310000},"page":"12647-12665","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-view latent structure learning with rank recovery"],"prefix":"10.1007","volume":"53","author":[{"given":"Jun","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihong","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"4141_CR1","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang M (2022) Multi-feature fusion partitioned local binary pattern method for finger vein recognition. Signal Image Video Process, 1\u20139","DOI":"10.1007\/s11760-021-02058-2"},{"key":"4141_CR2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ijleo.2016.11.046","volume":"131","author":"D Giveki","year":"2017","unstructured":"Giveki D, Soltanshahi MA, Montazer GA (2017) A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Optik 131:242\u2013254","journal-title":"Optik"},{"issue":"13","key":"4141_CR3","doi-asserted-by":"crossref","first-page":"3620","DOI":"10.1364\/AO.58.003620","volume":"58","author":"J Erazo-Aux","year":"2019","unstructured":"Erazo-Aux J, Loaiza-Correa H, Restrepo-Giron A (2019) Histograms of oriented gradients for automatic detection of defective regions in thermograms. Appl Opt 58(13):3620\u20133629","journal-title":"Appl Opt"},{"key":"4141_CR4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","volume":"38","author":"J Zhao","year":"2017","unstructured":"Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: recent progress and new challenges. Inform Fus 38:43\u201354","journal-title":"Inform Fus"},{"issue":"2","key":"4141_CR5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.26599\/BDMA.2018.9020003","volume":"1","author":"Y Yang","year":"2018","unstructured":"Yang Y, Wang H (2018) Multi-view clustering: a survey. Big Data Mining and Analytics 1 (2):83\u2013107","journal-title":"Big Data Mining and Analytics"},{"key":"4141_CR6","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.patcog.2018.11.015","volume":"88","author":"M Yang","year":"2019","unstructured":"Yang M, Deng C, Nie F (2019) Adaptive-weighting discriminative regression for multi-view classification. Pattern Recogn 88:236\u2013245","journal-title":"Pattern Recogn"},{"key":"4141_CR7","doi-asserted-by":"crossref","first-page":"49669","DOI":"10.1109\/ACCESS.2019.2910322","volume":"7","author":"T Shu","year":"2019","unstructured":"Shu T, Zhang B, Tang YY (2019) Multi-view classification via a fast and effective multi-view nearest-subspace classifier. IEEE Access 7:49669\u201349679","journal-title":"IEEE Access"},{"key":"4141_CR8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neucom.2016.10.089","volume":"253","author":"X Cheng","year":"2017","unstructured":"Cheng X, Zhu Y, Song J, Wen G, He W (2017) A novel low-rank hypergraph feature selection for multi-view classification. Neurocomputing 253:115\u2013121","journal-title":"Neurocomputing"},{"key":"4141_CR9","doi-asserted-by":"crossref","first-page":"114472","DOI":"10.1109\/ACCESS.2019.2934179","volume":"7","author":"MS Yang","year":"2019","unstructured":"Yang MS, Sinaga KP (2019) A feature-reduction multi-view k-means clustering algorithm. IEEE Access 7:114472\u2013114486","journal-title":"IEEE Access"},{"key":"4141_CR10","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.inffus.2020.12.007","volume":"70","author":"H Zhang","year":"2021","unstructured":"Zhang H, Wu D, Nie F, Wang R, Li X (2021) Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection. Inform Fus 70:129\u2013140","journal-title":"Inform Fus"},{"key":"4141_CR11","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.patcog.2019.04.024","volume":"93","author":"Q Yin","year":"2019","unstructured":"Yin Q, Zhang J, Wu S, Li H (2019) Multi-view clustering via joint feature selection and partially constrained cluster label learning. Pattern Recogn 93:380\u2013391","journal-title":"Pattern Recogn"},{"key":"4141_CR12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neucom.2020.08.049","volume":"471","author":"Z Li","year":"2022","unstructured":"Li Z, Hu Z, Nie F, Wang R, Li X (2022) Multi-view clustering based on generalized low rank approximation. Neurocomputing 471:251\u2013259","journal-title":"Neurocomputing"},{"issue":"12","key":"4141_CR13","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TKDE.2018.2875908","volume":"31","author":"X Fu","year":"2018","unstructured":"Fu X, Huang K, Papalexakis EE, Song H, Talukdar P, Sidiropoulos ND, Faloutsos C, Mitchell T (2018) Efficient and distributed generalized canonical correlation analysis for big multiview data. IEEE Trans Knowl Data Eng 31(12):2304\u20132318","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"4141_CR14","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s11063-018-9904-7","volume":"50","author":"H Tan","year":"2019","unstructured":"Tan H, Zhang X, Lan L, Huang X, Luo Z (2019) Nonnegative constrained graph based canonical correlation analysis for multi-view feature learning. Neural Process Lett 50(2):1215\u20131240","journal-title":"Neural Process Lett"},{"key":"4141_CR15","doi-asserted-by":"crossref","first-page":"29293","DOI":"10.1109\/ACCESS.2021.3056677","volume":"9","author":"W Cai","year":"2021","unstructured":"Cai W, Zhou H, Xu L (2021) A multi-view co-training clustering algorithm based on global and local structure preserving. IEEE Access 9:29293\u201329302","journal-title":"IEEE Access"},{"key":"4141_CR16","doi-asserted-by":"crossref","first-page":"5698","DOI":"10.1109\/TNNLS.2020.3027351","volume":"32","author":"M Chen","year":"2021","unstructured":"Chen M, Li X (2021) Robust matrix factorization with spectral embedding. IEEE Trans Neur Netw Learn Syst 32:5698\u20135707","journal-title":"IEEE Trans Neur Netw Learn Syst"},{"key":"4141_CR17","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ins.2021.06.069","volume":"576","author":"B Liu","year":"2021","unstructured":"Liu B, Chen X, Xiao Y, Li W, Liu L, Liu C (2021) An efficient dictionary-based multi-view learning method. Inform Sci 576:157\u2013172","journal-title":"Inform Sci"},{"issue":"6","key":"4141_CR18","doi-asserted-by":"crossref","first-page":"102694","DOI":"10.1016\/j.ipm.2021.102694","volume":"58","author":"MH Aghdam","year":"2021","unstructured":"Aghdam MH, Zanjani MD (2021) A novel regularized asymmetric non-negative matrix factorization for text clustering. Inform Process Manag 58(6):102694","journal-title":"Inform Process Manag"},{"key":"4141_CR19","doi-asserted-by":"crossref","first-page":"105582","DOI":"10.1016\/j.knosys.2020.105582","volume":"194","author":"N Liang","year":"2020","unstructured":"Liang N, Yang Z, Li Z, Sun W, Xie S (2020) Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints. Knowl-Based Syst 194:105582","journal-title":"Knowl-Based Syst"},{"key":"4141_CR20","doi-asserted-by":"crossref","unstructured":"Liu X, Pan G, Xie M (2021) Multi-view subspace clustering with adaptive locally consistent graph regularization. Neural Comput Applic, 1\u201316","DOI":"10.1007\/s00521-021-06166-5"},{"key":"4141_CR21","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ins.2020.06.068","volume":"544","author":"P Jing","year":"2021","unstructured":"Jing P, Su Y, Li Z, Nie L (2021) Learning robust affinity graph representation for multi-view clustering. Inform Sci 544:155\u2013167","journal-title":"Inform Sci"},{"key":"4141_CR22","doi-asserted-by":"crossref","first-page":"107375","DOI":"10.1016\/j.patcog.2020.107375","volume":"105","author":"P Zhou","year":"2020","unstructured":"Zhou P, Du L, Li X, Shen Y-D, Qian Y (2020) Unsupervised feature selection with adaptive multiple graph learning. Pattern Recogn 105:107375","journal-title":"Pattern Recogn"},{"key":"4141_CR23","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.ins.2021.03.059","volume":"568","author":"X Yu","year":"2021","unstructured":"Yu X, Liu H, Wu Y, Zhang C (2021) Fine-grained similarity fusion for multi-view spectral clustering. Inform Sci 568:350\u2013368","journal-title":"Inform Sci"},{"key":"4141_CR24","doi-asserted-by":"crossref","unstructured":"Dai J, Ren Z, Luo Y, Song H, Yang J (2021) Multi-view clustering with latent low-rank proxy graph learning. Cogn Comput, 1\u201312","DOI":"10.1007\/s12559-021-09889-8"},{"key":"4141_CR25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.inffus.2020.10.013","volume":"68","author":"M-S Chen","year":"2021","unstructured":"Chen M-S, Huang L, Wang C-D, Huang D, Lai J-H (2021) Relaxed multi-view clustering in latent embedding space. Inform Fus 68:8\u201321","journal-title":"Inform Fus"},{"key":"4141_CR26","doi-asserted-by":"crossref","unstructured":"Mi Y, Ren Z, Mukherjee M, Huang Y, Sun Q, Chen L (2021) Diversity and consistency embedding learning for multi-view subspace clustering. Appl Intell, 1\u201314","DOI":"10.1007\/s10489-020-02126-z"},{"key":"4141_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2017.10.023","volume":"294","author":"P Luo","year":"2018","unstructured":"Luo P, Peng J, Guan Z, Fan J (2018) Dual regularized multi-view non-negative matrix factorization for clustering. Neurocomputing 294:1\u201311","journal-title":"Neurocomputing"},{"key":"4141_CR28","doi-asserted-by":"crossref","first-page":"102578","DOI":"10.1016\/j.jvcir.2019.102578","volume":"63","author":"J Ma","year":"2019","unstructured":"Ma J, Yuan Y (2019) Dimension reduction of image deep feature using pca. J Vis Commun Image Represent 63:102578","journal-title":"J Vis Commun Image Represent"},{"key":"4141_CR29","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.ins.2020.07.059","volume":"547","author":"W Rong","year":"2021","unstructured":"Rong W, Zhuo E, Peng H, Chen J, Wang H, Han C, Cai H (2021) Learning a consensus affinity matrix for multi-view clustering via subspaces merging on grassmann manifold. Inform Sci 547:68\u201387","journal-title":"Inform Sci"},{"key":"4141_CR30","doi-asserted-by":"crossref","unstructured":"Mei Y, Ren Z, Wu B, Shao Y, Yang T (2021) Robust graph-based multi-view clustering in latent embedding space. Int J Mach Learn Cybern, 1\u201312","DOI":"10.1007\/s13042-021-01421-6"},{"key":"4141_CR31","doi-asserted-by":"crossref","first-page":"107524","DOI":"10.1016\/j.patcog.2020.107524","volume":"108","author":"R Fan","year":"2020","unstructured":"Fan R, Luo T, Zhuge W, Qiang S, Hou C (2020) Multi-view subspace learning via bidirectional sparsity. Pattern Recogn 108:107524","journal-title":"Pattern Recogn"},{"key":"4141_CR32","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neucom.2020.08.049","volume":"471","author":"Z Li","year":"2022","unstructured":"Li Z, Hu Z, Nie F, Wang R, Li X (2022) Multi-view clustering based on generalized low rank approximation. Neurocomputing 471:251\u2013259","journal-title":"Neurocomputing"},{"key":"4141_CR33","first-page":"125702","volume":"392","author":"M Fornasier","year":"2021","unstructured":"Fornasier M, Maly J, Naumova V (2021) Robust recovery of low-rank matrices with non-orthogonal sparse decomposition from incomplete measurements. Appl Math Comput 392:125702","journal-title":"Appl Math Comput"},{"key":"4141_CR34","doi-asserted-by":"crossref","first-page":"105514","DOI":"10.1016\/j.knosys.2020.105514","volume":"194","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Zhu J, Tian Z, Li Z, Pang S, Jia X (2020) Constrained bilinear factorization multi-view subspace clustering. Knowl-Based Syst 194:105514","journal-title":"Knowl-Based Syst"},{"key":"4141_CR35","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1016\/j.neucom.2017.11.021","volume":"275","author":"S Yu","year":"2018","unstructured":"Yu S, Yiquan W (2018) Subspace clustering based on latent low rank representation with frobenius norm minimization. Neurocomputing 275:2479\u20132489","journal-title":"Neurocomputing"},{"issue":"10","key":"4141_CR36","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/TKDE.2017.2725263","volume":"29","author":"W Zhuge","year":"2017","unstructured":"Zhuge W, Nie F, Hou C, Yi D (2017) Unsupervised single and multiple views feature extraction with structured graph. IEEE Trans Knowl Data Eng 29(10):2347\u20132359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4141_CR37","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.patcog.2017.08.024","volume":"73","author":"M Brbi\u0107","year":"2018","unstructured":"Brbi\u0107 M, Kopriva I (2018) Multi-view low-rank sparse subspace clustering. Pattern Recogn 73:247\u2013258","journal-title":"Pattern Recogn"},{"issue":"6","key":"4141_CR38","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TKDE.2019.2903810","volume":"32","author":"H Wang","year":"2019","unstructured":"Wang H, Yang Y, Liu B (2019) Gmc: graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116\u20131129","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4141_CR39","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.neucom.2019.10.074","volume":"379","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Zhu J, Li Z, Pang S, Wang J, Li Y (2020) Feature concatenation multi-view subspace clustering. Neurocomputing 379:89\u2013102","journal-title":"Neurocomputing"},{"key":"4141_CR40","doi-asserted-by":"crossref","unstructured":"Ma S, Zheng Q, Liu Y (2021) Essential multi-view graph learning for clustering. J Ambient Intell Humaniz Comput, 1\u201312","DOI":"10.1007\/s12652-021-03002-5"},{"key":"4141_CR41","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.neunet.2021.07.020","volume":"143","author":"W Hao","year":"2021","unstructured":"Hao W, Pang S, Chen Z (2021) Multi-view spectral clustering via common structure maximization of local and global representations. Neural Netw 143:595\u2013606","journal-title":"Neural Netw"},{"key":"4141_CR42","doi-asserted-by":"crossref","first-page":"108429","DOI":"10.1016\/j.patcog.2021.108429","volume":"124","author":"S Shi","year":"2022","unstructured":"Shi S, Nie F, Wang R, Li X (2022) Self-weighting multi-view spectral clustering based on nuclear norm. Pattern Recogn 124:108429","journal-title":"Pattern Recogn"},{"key":"4141_CR43","doi-asserted-by":"crossref","first-page":"106489","DOI":"10.1016\/j.knosys.2020.106489","volume":"210","author":"H Li","year":"2020","unstructured":"Li H, Ren Z, Mukherjee M, Huang Y, Sun Q, Li X, Chen L (2020) Robust energy preserving embedding for multi-view subspace clustering. Knowl-Based Syst 210:106489","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"4141_CR44","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1109\/TCYB.2018.2887094","volume":"50","author":"Z Kang","year":"2019","unstructured":"Kang Z, Pan H, Hoi SC, Xu Z (2019) Robust graph learning from noisy data. IEEE Trans Cybern 50(5):1833\u20131843","journal-title":"IEEE Trans Cybern"},{"key":"4141_CR45","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.neunet.2019.09.013","volume":"121","author":"D Xie","year":"2020","unstructured":"Xie D, Gao Q, Wang Q, Zhang X, Gao X (2020) Adaptive latent similarity learning for multi-view clustering. Neural Netw 121:409\u2013418","journal-title":"Neural Netw"},{"key":"4141_CR46","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.ins.2020.10.059","volume":"551","author":"X Zhang","year":"2021","unstructured":"Zhang X, Ren Z, Sun H, Bai K, Feng X, Liu Z (2021) Multiple kernel low-rank representation-based robust multi-view subspace clustering. Inform Sci 551:324\u2013340","journal-title":"Inform Sci"},{"key":"4141_CR47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.knosys.2018.06.016","volume":"160","author":"C Tang","year":"2018","unstructured":"Tang C, Chen J, Liu X, Li M, Wang P, Wang M, Lu P (2018) Consensus learning guided multi-view unsupervised feature selection. Knowl-Based Syst 160:49\u201360","journal-title":"Knowl-Based Syst"},{"key":"4141_CR48","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2021.01.039","volume":"437","author":"Y Yun","year":"2021","unstructured":"Yun Y, Xia W, Zhang Y, Gao Q, Gao X (2021) Self-representation and class-specificity distribution based multi-view clustering. Neurocomputing 437:9\u201320","journal-title":"Neurocomputing"},{"issue":"1","key":"4141_CR49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/TCYB.2018.2868742","volume":"50","author":"Y Pang","year":"2018","unstructured":"Pang Y, Xie J, Nie F, Li X (2018) Spectral clustering by joint spectral embedding and spectral rotation. IEEE Trans Cybern 50(1):247\u2013258","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"4141_CR50","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1109\/LCOMM.2019.2902147","volume":"23","author":"R Pal","year":"2019","unstructured":"Pal R, Chaitanya AK, Srinivas K (2019) Low-complexity beam selection algorithms for millimeter wave beamspace mimo systems. IEEE Commun Lett 23(4):768\u2013771","journal-title":"IEEE Commun Lett"},{"key":"4141_CR51","doi-asserted-by":"crossref","first-page":"105126","DOI":"10.1016\/j.knosys.2019.105126","volume":"189","author":"G-Y Zhang","year":"2020","unstructured":"Zhang G-Y, Zhou Y-R, He X-Y, Wang C-D, Huang D (2020) One-step kernel multi-view subspace clustering. Knowl-Based Syst 189:105126","journal-title":"Knowl-Based Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04141-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04141-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04141-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T10:46:41Z","timestamp":1684579601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04141-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,30]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["4141"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04141-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,30]]},"assertion":[{"value":"2 September 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}