{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T22:05:00Z","timestamp":1778709900740,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"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":["61903091"],"award-info":[{"award-number":["61903091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2020A1515010801"],"award-info":[{"award-number":["2020A1515010801"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s13042-022-01721-5","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T03:02:36Z","timestamp":1668999756000},"page":"1695-1709","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-level regularization-based unsupervised multi-view feature selection with adaptive graph learning"],"prefix":"10.1007","volume":"14","author":[{"given":"Tingjian","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoliang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loi Lei","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan Yan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"1721_CR1","doi-asserted-by":"publisher","first-page":"2843","DOI":"10.1007\/s13042-021-01370-0","volume":"12","author":"S Zhan","year":"2021","unstructured":"Zhan S, Sun W, Du C, Zhong W (2021) Diversity-promoting multi-view graph learning for semi-supervised classification. Int J Mach Learn Cybern 12:2843\u20132857","journal-title":"Int J Mach Learn Cybern"},{"key":"1721_CR2","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s13042-019-00992-9","volume":"11","author":"Z Jiang","year":"2020","unstructured":"Jiang Z, Bian Z, Wang S (2020) Multi-view local linear KNN classification: theoretical and experimental studies on image classification. Int J Mach Learn Cybern 11:525\u2013543","journal-title":"Int J Mach Learn Cybern"},{"key":"1721_CR3","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1007\/s13042-021-01297-6","volume":"12","author":"H Yin","year":"2021","unstructured":"Yin H, Hu W, Li F, Lou J (2021) One-step multi-view spectral clustering by learning common and specific nonnegative embeddings. Int J Mach Learn Cybern 12:2121\u20132134","journal-title":"Int J Mach Learn Cybern"},{"issue":"6","key":"1721_CR4","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1109\/TSMCB.2009.2039566","volume":"40","author":"T Xia","year":"2010","unstructured":"Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B (Cybern) 40(6):1438\u20131446","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"1721_CR5","doi-asserted-by":"crossref","unstructured":"Li L, Cai M (2009) Drug target prediction by multi-view low rank embedding. IEEE\/ACM Trans Comput Biol Bioinform 16(5):1712\u20131721","DOI":"10.1109\/TCBB.2017.2706267"},{"issue":"2","key":"1721_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1049\/iet-syb.2013.0040","volume":"8","author":"L Li","year":"2014","unstructured":"Li L (2014) MPGraph: multi-view penalised graph clustering for predicting drug-target interactions. IET Syst Biol 8(2):67\u201373","journal-title":"IET Syst Biol"},{"key":"1721_CR7","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893","DOI":"10.1109\/CVPR.2005.177"},{"issue":"3","key":"1721_CR8","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145\u2013175","journal-title":"Int J Comput Vis"},{"issue":"7","key":"1721_CR9","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1721_CR10","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46(2):450\u2013461","journal-title":"IEEE Trans Cybern"},{"issue":"11","key":"1721_CR11","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/TMM.2020.2966887","volume":"22","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wu J, Cai Z, Yu PS (2020) Multi-view multi-label learning with sparse feature selection for image annotation. IEEE Trans Multimed 22(11):2844\u20132857","journal-title":"IEEE Trans Multimed"},{"key":"1721_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.inffus.2018.10.004","volume":"50","author":"S Sun","year":"2019","unstructured":"Sun S, Liu Y, Liang M (2019) Multi-view learning for visual violence recognition with maximum entropy discrimination and deep features. Inform Fus 50:43\u201353","journal-title":"Inform Fus"},{"key":"1721_CR13","doi-asserted-by":"crossref","unstructured":"Liu H, Mao H, Fu Y (2016) Robust multi-view feature selection. In: IEEE 16th International Conference on Data Mining, pp. 281-290","DOI":"10.1109\/ICDM.2016.0039"},{"issue":"10","key":"1721_CR14","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.1109\/TPAMI.2020.2987013","volume":"43","author":"M Komeili","year":"2021","unstructured":"Komeili M, Armanfard N, Hatzinakos D (2021) Multiview feature selection for single-view classification. IEEE Trans Pattern Anal Mach Intell 43(10):3573\u20133586","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1721_CR15","unstructured":"He X, Niyogi P (2003) Locality preserving projections. Adv Neural Inform Process Syst: 153\u2013160"},{"key":"1721_CR16","unstructured":"Han D, Kim J (2015) Unsupervised simultaneous orthogonal basis clustering feature selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5016-5023"},{"key":"1721_CR17","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"1721_CR18","doi-asserted-by":"crossref","unstructured":"Tang J, Hu X, Gao H, Liu H (2013) Unsupervised feature selection for multi-view data in social media. In: SIAM International Conference on Data Mining, pp. 270-278","DOI":"10.1137\/1.9781611972832.30"},{"key":"1721_CR19","unstructured":"Yang Y, Shen HT, Ma Z, Huang Z, Zhou X (2011) $$l_{2,1}$$-norm regularized discriminative feature selection for unsupervised learning. In: International Joint Conference on Artificial Intelligence, pp. 1589-1594"},{"issue":"7","key":"1721_CR20","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1109\/TNNLS.2016.2551724","volume":"28","author":"J Gui","year":"2017","unstructured":"Gui J, Sun Z, Ji S, Tao D, Tan T (2017) Feature selection based on structured sparsity: a comprehensive study. IEEE Trans Neural Netw Learn Syst 28(7):1490\u20131507","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"1721_CR21","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","volume":"20","author":"PA Estevez","year":"2009","unstructured":"Estevez PA, Tesmer M, Perez CA, Zurada JM (2009) Normalized mutual information feature selection. IEEE Trans Neural Netw 20(2):189\u2013201","journal-title":"IEEE Trans Neural Netw"},{"key":"1721_CR22","doi-asserted-by":"publisher","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":"1721_CR23","doi-asserted-by":"publisher","unstructured":"Tang C, Zheng X, Liu X, Zhang W, Zhang J, Xiong J, Wang L, Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2020.3048678","DOI":"10.1109\/TKDE.2020.3048678"},{"issue":"9","key":"1721_CR24","doi-asserted-by":"publisher","first-page":"2138","DOI":"10.1109\/TKDE.2013.65","volume":"26","author":"Z Li","year":"2014","unstructured":"Li Z, Liu J, Yang Y, Zhou X, Lu H (2014) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Knowl Data Eng 26(9):2138\u20132150","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1721_CR25","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neucom.2017.02.034","volume":"241","author":"S Du","year":"2017","unstructured":"Du S, Ma Y, Li S, Ma Y (2017) Robust unsupervised feature selection via matrix factorization. Neurocomputing 241:115\u2013127","journal-title":"Neurocomputing"},{"issue":"3","key":"1721_CR26","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TKDE.2017.2763618","volume":"30","author":"X Zhu","year":"2018","unstructured":"Zhu X, Zhang S, Hu R, Zhu Y, Song J (2018) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517\u2013529","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1721_CR27","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.knosys.2016.09.006","volume":"112","author":"R Shang","year":"2016","unstructured":"Shang R, Wang W, Stolkin R, Jiao L (2016) Subspace learning-based graph regularized feature selection. Knowl Based Syst 112:152\u2013165","journal-title":"Knowl Based Syst"},{"issue":"1","key":"1721_CR28","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1109\/TGRS.2010.2054832","volume":"49","author":"X Chen","year":"2011","unstructured":"Chen X, Fang T, Huo H, Li D (2011) Graph-based feature selection for object-oriented classification in VHR airborne imagery. IEEE Trans Geosci Remote Sens 49(1):353\u2013365","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1721_CR29","doi-asserted-by":"crossref","unstructured":"Shao W, He L, Lu C, Wei X, Yu PS (2016) Online unsupervised multi-view feature selection. In: IEEE 16th International Conference on Data Mining, pp. 1203-1208","DOI":"10.1109\/ICDM.2016.0160"},{"key":"1721_CR30","doi-asserted-by":"crossref","unstructured":"Feng Y, Xiao J, Zhuang Y, Liu X (2012) Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Asian conference on Computer Vision, pp. 343-357","DOI":"10.1007\/978-3-642-37331-2_26"},{"key":"1721_CR31","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.sigpro.2014.11.015","volume":"120","author":"Z Wang","year":"2016","unstructured":"Wang Z, Feng Y, Qi T, Yang X, Zhang JJ (2016) Adaptive multi-view feature selection for human motion retrieval. Signal Process 120:691\u2013701","journal-title":"Signal Process"},{"issue":"3","key":"1721_CR32","first-page":"1210","volume":"33","author":"F Nie","year":"2021","unstructured":"Nie F, Zhu W, Li X (2021) Structured graph optimization for unsupervised feature selection. IEEE Trans Knowl Data Eng 33(3):1210\u20131222","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1721_CR33","unstructured":"He X, Cai D, Niyogi P (2005) Laplacian Score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507\u2013514"},{"key":"1721_CR34","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: International Conference on Machine Learning, pp. 1151-1157","DOI":"10.1145\/1273496.1273641"},{"key":"1721_CR35","doi-asserted-by":"crossref","unstructured":"Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: ACM SIGKDD International Conference on Knowledge discovery and data mining, pp. 333\u2013342","DOI":"10.1145\/1835804.1835848"},{"key":"1721_CR36","doi-asserted-by":"crossref","unstructured":"Li Z, Yang Y, Liu J, Zhou X, Lu H (2012) Unsupervised feature selection using nonnegative spectral analysis. In: AAAI Conference on Artificial Intelligence, pp. 1026\u20131032","DOI":"10.1609\/aaai.v26i1.8289"},{"issue":"3","key":"1721_CR37","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TSMC.2016.2605132","volume":"48","author":"S Wang","year":"2018","unstructured":"Wang S, Zhu W (2018) Sparse graph embedding unsupervised feature selection. IEEE Trans Syst Man Cybern Syst 48(3):329\u2013341","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1721_CR38","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.inffus.2020.04.005","volume":"62","author":"X Xie","year":"2020","unstructured":"Xie X, Sun S (2020) General multi-view semi-supervised least squares support vector machines with multi-manifold regularization. Inform Fus 62:63\u201372","journal-title":"Inform Fus"},{"key":"1721_CR39","doi-asserted-by":"publisher","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"},{"issue":"9","key":"1721_CR40","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1109\/TKDE.2017.2681670","volume":"29","author":"C Hou","year":"2017","unstructured":"Hou C, Nie F, Tao H, Yi D (2017) Multi-view unsupervised feature selection with adaptive similarity and view weight. IEEE Trans Knowl Data Eng 29(9):1998\u20132011","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1721_CR41","doi-asserted-by":"crossref","unstructured":"Dong X, Zhu L, Song X, Li J, Cheng Z (2018) Adaptive collaborative similarity learning for unsupervised multi-view feature selection. In: International Joint Conference on Artificial Intelligence, pp. 2064-2070","DOI":"10.24963\/ijcai.2018\/285"},{"issue":"2","key":"1721_CR42","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.patcog.2014.08.006","volume":"48","author":"P Zhu","year":"2015","unstructured":"Zhu P, Zuo W, Zhang L, Hu Q, Shiu SCK (2015) Unsupervised feature selection by regularized self-representation. Pattern Recogn 48(2):438\u2013446","journal-title":"Pattern Recogn"},{"key":"1721_CR43","doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 977-986","DOI":"10.1145\/2623330.2623726"},{"issue":"11","key":"1721_CR44","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1073\/pnas.35.11.652","volume":"35","author":"K Fan","year":"1949","unstructured":"Fan K (1949) On a theorem of Weyl concerning eigenvalues of linear transformations. Proc Natl Acad Sci USA 35(11):652\u2013655","journal-title":"Proc Natl Acad Sci USA"},{"key":"1721_CR45","doi-asserted-by":"crossref","unstructured":"Xu C, Guan Z, Zhao W, Liu Y, Wang Q, Wang Z (2018) Deep Multi view concept learning. In: International Joint Conference on Artificial Intelligence, pp. 2898-2904","DOI":"10.24963\/ijcai.2018\/402"},{"key":"1721_CR46","doi-asserted-by":"crossref","unstructured":"Nie F, Zhu W, Li X (2016) Unsupervised feature selection with structured graph optimization. In: AAAI Conference on Artificial Intelligence, pp. 1302-1308","DOI":"10.1609\/aaai.v30i1.10168"},{"issue":"8","key":"1721_CR47","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1721_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107663","volume":"111","author":"H Lim","year":"2021","unstructured":"Lim H, Kim D-W (2021) Pairwise dependence-based unsupervised feature selection. Pattern Recogn 111:107663","journal-title":"Pattern Recogn"},{"key":"1721_CR49","doi-asserted-by":"publisher","first-page":"4143","DOI":"10.1109\/TIP.2021.3062692","volume":"30","author":"B Zhang","year":"2021","unstructured":"Zhang B, Qiang Q, Wang F, Nie F (2021) Flexible multi-view unsupervised graph embedding. IEEE Trans Image Process 30:4143\u20134156","journal-title":"IEEE Trans Image Process"},{"key":"1721_CR50","unstructured":"Bache K, Lichman M (2013) Uci machine learning repository"},{"key":"1721_CR51","doi-asserted-by":"crossref","unstructured":"Xu J, Han J, Nie F (2016) Discriminatively embedded k-means for multi-view clustering, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5356-5364","DOI":"10.1109\/CVPR.2016.578"},{"key":"1721_CR52","doi-asserted-by":"crossref","unstructured":"Samaria F, Harter A (1994) Parameterisation of a stochastic model for human face identification. In IEEE Workshop on Applications of Computer Vision, pp. 138\u2013142","DOI":"10.1109\/ACV.1994.341300"},{"issue":"1\u20132","key":"1721_CR53","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0004-3702(00)00004-7","volume":"118","author":"M Craven","year":"2000","unstructured":"Craven M, DiPasquo D, Freitag D, McCallum A, Mitchell T, Nigam K, Slattery S (2000) Learning to construct knowledge bases from the World Wide Web. Artif Intell 118(1\u20132):69\u2013113","journal-title":"Artif Intell"},{"issue":"3","key":"1721_CR54","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2005.55","volume":"27","author":"X He","year":"2005","unstructured":"He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using Laplacianfaces. EEE Trans Pattern Anal Mach Intell 27(3):328\u2013340","journal-title":"EEE Trans Pattern Anal Mach Intell"},{"key":"1721_CR55","doi-asserted-by":"crossref","unstructured":"Lampert CH, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 951\u2013958","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"1721_CR56","doi-asserted-by":"publisher","unstructured":"Xu C, Liu H, Guan Z, Wu X, Tan J, Ling B, Adversarial incomplete multiview subspace clustering networks. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2021.3062830","DOI":"10.1109\/TCYB.2021.3062830"},{"key":"1721_CR57","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.neucom.2020.01.044","volume":"387","author":"X Bai","year":"2020","unstructured":"Bai X, Zhu L, Liang C, Li J, Nie X, Chang X (2020) Multi-view feature selection via Nonnegative Structured Graph Learning. Neurocomputing 387:110\u2013122","journal-title":"Neurocomputing"},{"key":"1721_CR58","doi-asserted-by":"crossref","unstructured":"Kuhn HW (2005) The hungarian method for the assignment problem. Naval Res Log 52(1)","DOI":"10.1002\/nav.20053"},{"key":"1721_CR59","doi-asserted-by":"crossref","unstructured":"Boyed S, Vandenberghe L (2004) Convex optimization. Cambridge University Press","DOI":"10.1017\/CBO9780511804441"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01721-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01721-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01721-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T04:09:46Z","timestamp":1681963786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01721-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,21]]},"references-count":59,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["1721"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01721-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,21]]},"assertion":[{"value":"28 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}