{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:25:37Z","timestamp":1740122737008,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"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":["11971231","1211530001"],"award-info":[{"award-number":["11971231","1211530001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11063-022-10973-9","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T12:06:56Z","timestamp":1661256416000},"page":"3923-3952","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Incomplete Multi-view Learning via Consensus Graph Completion"],"prefix":"10.1007","volume":"55","author":[{"given":"Heng","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xiaohong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Enhao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"10973_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","volume":"38","author":"J Zhao","year":"2017","unstructured":"Zhao J, Xie XJ, Xu X, Sun SL (2017) Multi-view learning overview: recent progress and new challenges. Inf Fusion 38:43\u201354","journal-title":"Inf Fusion"},{"key":"10973_CR2","doi-asserted-by":"publisher","first-page":"29293","DOI":"10.1109\/ACCESS.2021.3056677","volume":"9","author":"WL Cai","year":"2021","unstructured":"Cai WL, Zhou HH, 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":"10973_CR3","unstructured":"Kumar A, Daum\u00e9 H (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 393\u2013400"},{"issue":"9","key":"10973_CR4","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1109\/TCSVT.2011.2130270","volume":"21","author":"C Liu","year":"2011","unstructured":"Liu C, Yuen PC (2011) A boosted co-training algorithm for human action recognition. IEEE Trans Circuits Syst Video Technol 21(9):1203\u20131213","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"6","key":"10973_CR5","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1109\/TKDE.2019.2958342","volume":"33","author":"XH Yang","year":"2021","unstructured":"Yang XH, Liu WF, Liu W, Tao DC (2021) A survey on canonical correlation analysis. IEEE Trans Knowl Data Eng 33(6):2349\u20132368","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10973_CR6","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.patcog.2017.08.024","volume":"73","author":"M Brbic","year":"2018","unstructured":"Brbic M, Kopriva I (2018) Multi-view low-rank sparse subspace clustering. Pattern Recogn 73:247\u2013258","journal-title":"Pattern Recogn"},{"key":"10973_CR7","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.patcog.2018.01.012","volume":"78","author":"Y Zhao","year":"2018","unstructured":"Zhao Y, You X, Yu S, Xu C, Yuan W, Jing X-Y, Zhang T, Tao D (2018) Multi-view manifold learning with locality alignment. Pattern Recogn 78:154\u2013166","journal-title":"Pattern Recogn"},{"key":"10973_CR8","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2018.12.004","volume":"332","author":"XJ Xie","year":"2019","unstructured":"Xie XJ, Sun SL (2019) General multi-view learning with maximum entropy discrimination. Neurocomputing 332:184\u2013192","journal-title":"Neurocomputing"},{"key":"10973_CR9","doi-asserted-by":"crossref","unstructured":"Liu XW, Dou Y, Yin JP, Wang L, Zhu E (2016) Multiple kernel k-means clustering with matrix-induced regularization. In: 30th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) conference on artificial intelligence, pp 1888\u20131894","DOI":"10.1609\/aaai.v30i1.10249"},{"issue":"3","key":"10973_CR10","doi-asserted-by":"publisher","first-page":"481","DOI":"10.3233\/IDA-160816","volume":"20","author":"GQ Chao","year":"2016","unstructured":"Chao GQ, Sun SL (2016) Multi-kernel maximum entropy discrimination for multi-view learning. Intell Data Anal 20(3):481\u2013493","journal-title":"Intell Data Anal"},{"issue":"2","key":"10973_CR11","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/TNNLS.2020.2979532","volume":"32","author":"W Zhao","year":"2021","unstructured":"Zhao W, Xu C, Guan ZY, Liu Y (2021) Multiview concept learning via deep matrix factorization. IEEE Trans Neural Netw Learn Syst 32(2):814\u2013825","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10973_CR12","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","volume":"448","author":"XQ Yan","year":"2021","unstructured":"Yan XQ, Hu SZ, Mao YQ, Ye YD, Yu H (2021) Deep multi-view learning methods: a review. Neurocomputing 448:106\u2013129","journal-title":"Neurocomputing"},{"issue":"1","key":"10973_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TNNLS.2020.2977497","volume":"32","author":"G Sun","year":"2021","unstructured":"Sun G, Cong Y, Zhang YL, Zhao GS, Fu Y (2021) Continual multiview task learning via deep matrix factorization. IEEE Trans Neural Netw Learn Syst 32(1):139\u2013150","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10973_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3103979","author":"G Tan","year":"2021","unstructured":"Tan G, Wang Z, Shi Z (2021) Proportional-integral state estimator for quaternion-valued neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3103979","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10973_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101953","author":"Y Liu","year":"2021","unstructured":"Liu Y, Fan L, Zhang C, Zhou T, Xiao Z, Geng L, Shen D (2021) Incomplete multi-modal representation learning for Alzheimer\u2019s disease diagnosis. Med Image Anal. https:\/\/doi.org\/10.1016\/j.media.2020.101953","journal-title":"Med Image Anal"},{"issue":"12","key":"10973_CR16","doi-asserted-by":"publisher","first-page":"6276","DOI":"10.1109\/TNNLS.2018.2828699","volume":"29","author":"WQ Yang","year":"2018","unstructured":"Yang WQ, Shi YH, Gao Y, Wang L, Yang M (2018) Incomplete-data oriented multiview dimension reduction via sparse low-rank representation. IEEE Trans Neural Netw Learn Syst 29(12):6276\u20136291","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10973_CR17","doi-asserted-by":"crossref","unstructured":"Li SY, Jiang Y, Zhou ZH (2014) Partial multi-view clustering. In: 28th AAAI conference on artificial intelligence, pp. 1968\u20131974","DOI":"10.1609\/aaai.v28i1.8973"},{"key":"10973_CR18","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.neunet.2020.10.014","volume":"133","author":"J Wen","year":"2021","unstructured":"Wen J, Sun HJ, Fei LK, Li JX, Zhang Z, Zhang B (2021) Consensus guided incomplete multi-view spectral clustering. Neural Netw 133:207\u2013219","journal-title":"Neural Netw"},{"issue":"1","key":"10973_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TCYB.2018.2863790","volume":"50","author":"P Li","year":"2020","unstructured":"Li P, Chen SC (2020) Shared Gaussian process latent variable model for incomplete multiview clustering. IEEE Trans Cybern 50(1):61\u201373","journal-title":"IEEE Trans Cybern"},{"key":"10973_CR20","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2018.05.084","volume":"312","author":"LS Qiao","year":"2018","unstructured":"Qiao LS, Zhang LM, Chen SC, Shen DG (2018) Data-driven graph construction and graph learning: a review. Neurocomputing 312:336\u2013351","journal-title":"Neurocomputing"},{"issue":"2","key":"10973_CR21","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","volume":"2","author":"X Feng","year":"2021","unstructured":"Feng X, Ke S, Shuo Y, Aziz A, Liangtian W, Shirui P, Huan L (2021) Graph learning: a survey. IEEE Trans Artif Intell 2(2):109\u2013127","journal-title":"IEEE Trans Artif Intell"},{"issue":"4","key":"10973_CR22","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TCYB.2018.2884715","volume":"50","author":"J Wen","year":"2020","unstructured":"Wen J, Xu Y, Liu H (2020) Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern 50(4):1418\u20131429","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"10973_CR23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TCYB.2020.2987164","volume":"51","author":"J Wen","year":"2021","unstructured":"Wen J, Zhang Z, Zhang Z, Fei LK, Wang M (2021) Generalized incomplete multiview clustering with flexible locality structure diffusion. IEEE Trans Cybern 51(1):101\u2013114","journal-title":"IEEE Trans Cybern"},{"key":"10973_CR24","doi-asserted-by":"publisher","first-page":"108412","DOI":"10.1016\/j.patcog.2021.108412","volume":"123","author":"N Zhang","year":"2022","unstructured":"Zhang N, Sun S (2022) Incomplete multiview nonnegative representation learning with multiple graphs. Pattern Recogn 123:108412","journal-title":"Pattern Recogn"},{"key":"10973_CR25","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TMM.2020.3013408","volume":"23","author":"J Wen","year":"2021","unstructured":"Wen J, Yan K, Zhang Z, Xu Y, Wang JQ, Fei LK, Zhang B (2021) Adaptive graph completion based incomplete multi-view clustering. IEEE Trans Multimedia 23:2493\u20132504","journal-title":"IEEE Trans Multimedia"},{"issue":"11","key":"10973_CR26","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TSP.2019.2910475","volume":"67","author":"J Chen","year":"2019","unstructured":"Chen J, Wang G, Giannakis GB (2019) Graph multiview canonical correlation analysis. IEEE Trans Signal Process 67(11):2826\u20132838","journal-title":"IEEE Trans Signal Process"},{"key":"10973_CR27","volume-title":"Kernel methods for pattern analysis","author":"J Shawe-Taylor","year":"2005","unstructured":"Shawe-Taylor J, Cristianini N (2005) Kernel methods for pattern analysis. Cambridge University Press, Cambridge"},{"issue":"1","key":"10973_CR28","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s11063-020-10285-w","volume":"52","author":"EH Zhang","year":"2020","unstructured":"Zhang EH, Chen XH, Wang LP (2020) Consistent discriminant correlation analysis. Neural Process Lett 52(1):891\u2013904","journal-title":"Neural Process Lett"},{"issue":"3","key":"10973_CR29","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TNN.2007.891186","volume":"18","author":"C Wang","year":"2007","unstructured":"Wang C (2007) Variational Bayesian approach to canonical correlation analysis. IEEE Trans Neural Netw 18(3):905\u2013910","journal-title":"IEEE Trans Neural Netw"},{"key":"10973_CR30","doi-asserted-by":"crossref","unstructured":"Carroll JD (1968) Generalization of canonical correlation analysis to three or more sets of variables. In: Proceedings of the 76th annual convention of the american psychological association, vol 3, pp 227\u2013228","DOI":"10.1037\/e473742008-115"},{"issue":"16","key":"10973_CR31","doi-asserted-by":"publisher","first-page":"4150","DOI":"10.1109\/TSP.2017.2698365","volume":"65","author":"X Fu","year":"2017","unstructured":"Fu X, Huang KJ, Hong MY, Sidiropoulos ND, So AMC (2017) Scalable and flexible multiview max-var canonical correlation analysis. IEEE Trans Signal Process 65(16):4150\u20134165","journal-title":"IEEE Trans Signal Process"},{"issue":"11","key":"10973_CR32","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1109\/TKDE.2015.2445757","volume":"27","author":"Y Luo","year":"2015","unstructured":"Luo Y, Tao DC, Ramamohanarao K, Xu C, Wen YG (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111\u20133124","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"10973_CR33","first-page":"1191","volume":"42","author":"XW Liu","year":"2020","unstructured":"Liu XW, Zhu XZ, Li MM, Wang L, Zhu E, Liu TL, Kloft M, Shen DG, Yin JP, Gao W (2020) Multiple kernel k-means with incomplete kernels. IEEE Trans Pattern Anal Mach Intell 42(5):1191\u20131204","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10973_CR34","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1137\/070697835","volume":"52","author":"B Recht","year":"2010","unstructured":"Recht B, Fazel M, Parrilo PA (2010) Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev 52(3):471\u2013501","journal-title":"SIAM Rev"},{"key":"10973_CR35","doi-asserted-by":"crossref","unstructured":"Fazel M, Hindi H, Boyd SP (2001) Aacc, Aacc, Aacc: a rank minimization heuristic with application to minimum order system approximation. In: American Control Conference (ACC). Proceedings of the American Control Conference. IEEE, New York, pp 4734\u20134739","DOI":"10.1109\/ACC.2001.945730"},{"key":"10973_CR36","unstructured":"Kim E, Lee M, Oh S. Elastic-net regularization of singular values for robust subspace learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 915\u2013923"},{"issue":"11","key":"10973_CR37","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/S0165-1684(02)00300-6","volume":"82","author":"EJ Candes","year":"2002","unstructured":"Candes EJ, Guo F (2002) New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction. Signal Process 82(11):1519\u20131543","journal-title":"Signal Process"},{"issue":"4","key":"10973_CR38","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"JF Cai","year":"2010","unstructured":"Cai JF, Candes EJ, Shen ZW (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956\u20131982","journal-title":"SIAM J Optim"},{"issue":"2","key":"10973_CR39","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1137\/S106482750037322X","volume":"23","author":"TG Wright","year":"2001","unstructured":"Wright TG, Trefethen LN (2001) Large-scale computation of pseudospectra using ARPACK and eigs. SIAM J Sci Comput 23(2):591\u2013605","journal-title":"SIAM J Sci Comput"},{"key":"10973_CR40","doi-asserted-by":"crossref","unstructured":"Maurer A (2006) The rademacher complexity of linear transformation classes. In: Lugosi G, Simon HU (eds) Learning theory, proceedings. Lecture notes in artificial intelligence, vol 40, pp 65\u201378","DOI":"10.1007\/11776420_8"},{"issue":"10","key":"10973_CR41","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1162\/NECO_a_00872","volume":"28","author":"TL Liu","year":"2016","unstructured":"Liu TL, Tao DC, Xu D (2016) Dimensionality-dependent generalization bounds for k-dimensional coding schemes. Neural Comput 28(10):2213\u20132249","journal-title":"Neural Comput"},{"issue":"11","key":"10973_CR42","doi-asserted-by":"publisher","first-page":"5839","DOI":"10.1109\/TIT.2010.2069250","volume":"56","author":"A Maurer","year":"2010","unstructured":"Maurer A, Pontil M (2010) K-dimensional coding schemes in hilbert spaces. IEEE Trans Inf Theory 56(11):5839\u20135846","journal-title":"IEEE Trans Inf Theory"},{"key":"10973_CR43","unstructured":"Zhao H, Liu H, Fu Y. Incomplete multi-modal visual data grouping. In: IJCAI, pp 2392\u20132398"},{"key":"10973_CR44","doi-asserted-by":"crossref","unstructured":"Wen J, Zhang Z, Xu Y, Zhong ZF (2018) Incomplete multi-view clustering via graph regularized matrix factorization. In: Computer Vision\u2014ECCV 2018 workshops, Pt Iv, vol 11132, pp 593\u2013608","DOI":"10.1007\/978-3-030-11018-5_47"},{"key":"10973_CR45","doi-asserted-by":"crossref","unstructured":"Candes EJ, Recht B (2008) Exact low-rank matrix completion via convex optimization. In: 46th annual allerton conference on communication, control, and computing, pp 806\u2013827","DOI":"10.1109\/ALLERTON.2008.4797640"},{"issue":"3","key":"10973_CR46","first-page":"463","volume":"3","author":"PL Bartlett","year":"2003","unstructured":"Bartlett PL, Mendelson S (2003) Rademacher and Gaussian complexities: risk bounds and structural results. J Mach Learn Res 3(3):463\u2013482","journal-title":"J Mach Learn Res"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10973-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T16:35:13Z","timestamp":1690821313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10973-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10973"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10973-9","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,8,23]]},"assertion":[{"value":"14 July 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}