{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T19:59:38Z","timestamp":1760385578635,"version":"3.37.3"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61103138","61702163"],"award-info":[{"award-number":["61103138","61702163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan International Cooperation Project","award":["152102410036"],"award-info":[{"award-number":["152102410036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2017]]},"DOI":"10.1109\/access.2017.2767818","type":"journal-article","created":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T18:38:46Z","timestamp":1509475126000},"page":"24895-24907","source":"Crossref","is-referenced-by-count":9,"title":["Retargeted Multi-View Feature Learning With Separate and Shared Subspace Uncovering"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5487-9845","authenticated-orcid":false,"given":"Guo-Sen","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Bo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiexin","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0268-3"},{"key":"ref72","first-page":"1743","article-title":"Manifold alignment preserving global geometry","author":"wang","year":"2013","journal-title":"Proc IJCAI"},{"key":"ref71","first-page":"1882","article-title":"Intra-view and inter-view supervised correlation analysis for multi-view feature learning","author":"jing","year":"2014","journal-title":"Proc 28th AAAI Conf Artif Intell"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-008-0127-5"},{"article-title":"Multi-view dimensionality reduction via canonical correlation analysis","year":"2008","author":"foster","key":"ref74"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2605687"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-309"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5150-6"},{"key":"ref32","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"rakotomamonjy","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273594"},{"key":"ref30","first-page":"1531","article-title":"Large scale multiple kernel learning","volume":"7","author":"sonnenburg","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref37","first-page":"1825","article-title":"An extended level method for efficient multiple kernel learning","author":"xu","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553510"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.98"},{"key":"ref34","first-page":"396","article-title":"Learning non-linear combinations of kernels","author":"cortes","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Spectral regression A regression framework for efficient regularized subspace learning","year":"2009","author":"urbana-champaign","key":"ref60"},{"article-title":"Experiments on high resolution images towards outdoor scene classification","year":"2002","author":"monadjemi","key":"ref62"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.322"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015424"},{"journal-title":"UCI Machine Learning Repository","year":"2007","author":"asuncion","key":"ref64"},{"key":"ref27","article-title":"Non-sparse multiple kernel learning","author":"kloft","year":"2008","journal-title":"Proceedings of the NIPS Workshop on Kernel Learning Automatic Selection of Optimal Kernels"},{"key":"ref65","first-page":"953","article-title":"lp-norm multiple kernel learning","volume":"12","author":"kloft","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1142\/9789812776655"},{"key":"ref29","first-page":"1273","article-title":"A general and efficient multiple kernel learning algorithm","author":"sonnenburg","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref67","first-page":"719","article-title":"Multi-class discriminant kernel learning via convex programming","volume":"9","author":"ye","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459169"},{"key":"ref69","first-page":"1","article-title":"Multi-view canonical correlation analysis","author":"rupnik","year":"2010","journal-title":"Proc Conf Data Mining Data Warehouses (SiKDD)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995407"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"ref22","first-page":"435","article-title":"Active + semi-supervised learning = robust multi-view learning","author":"muslea","year":"2002","journal-title":"Proc ICML"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015350"},{"key":"ref24","first-page":"1135","article-title":"A new analysis of co-training","author":"wang","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref23","first-page":"2649","article-title":"Bayesian co-training","volume":"12","author":"yu","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref26","first-page":"27","article-title":"Learning the kernel matrix with semidefinite programming","volume":"5","author":"lanckriet","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.12.003"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972788.74"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.64"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TKDE.2007.190669","article-title":"SRDA: An efficient algorithm for large-scale discriminant analysis","volume":"20","author":"cai","year":"2008","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"ref57","first-page":"2810","article-title":"Multi-view correlated feature learning by uncovering shared component","author":"xue","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00977"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2371492"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206576"},{"key":"ref53","first-page":"361","article-title":"Predictive subspace learning for multi-view data: A large margin approach","author":"chen","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2435740"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25856-5_16"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-014-0563-8"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/28.3-4.321"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"ref14","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"journal-title":"A Survey on Multi-view Learning","year":"2013","author":"xu","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1362-6"},{"key":"ref3","first-page":"1","article-title":"Visual categorization with bags of keypoints","volume":"1","author":"csurka","year":"2004","journal-title":"Workshop on Statistical Learning in Computer Vision (ECCV)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.185"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/0899766042321814"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.29"},{"key":"ref7","first-page":"2750","article-title":"Large-scale multi-view spectral clustering via bipartite graph","author":"li","year":"2015","journal-title":"Proc 29th AAAI Conf Artif Intell"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72927-3_8"},{"key":"ref9","first-page":"387","article-title":"Bayesian multiview dimensionality reduction for learning predictive subspaces","author":"g\u00f6nen","year":"2014","journal-title":"Proc 21st Conf Artif Intell"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2006.09.011"},{"key":"ref45","first-page":"1247","article-title":"Deep canonical correlation analysis","author":"andrew","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553391"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2445757"},{"key":"ref42","first-page":"199","article-title":"Structured sparse canonical correlation analysis","author":"chen","year":"2012","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1142\/S012906570000034X"},{"key":"ref44","first-page":"965","article-title":"Bayesian canonical correlation analysis","volume":"14","author":"klami","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5222-7"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/7859429\/08091111.pdf?arnumber=8091111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T02:59:41Z","timestamp":1633921181000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8091111\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":74,"URL":"https:\/\/doi.org\/10.1109\/access.2017.2767818","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2017]]}}}