{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T05:50:58Z","timestamp":1770702658927,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T00:00:00Z","timestamp":1502668800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"crossref","award":["16ZR1414500"],"award-info":[{"award-number":["16ZR1414500"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602296"],"award-info":[{"award-number":["61602296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2019,5]]},"DOI":"10.1007\/s10044-017-0644-5","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T03:51:25Z","timestamp":1502682685000},"page":"457-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Weight-based canonical sparse cross-view correlation analysis"],"prefix":"10.1007","volume":"22","author":[{"given":"Changming","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Rigui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Zu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,8,14]]},"reference":[{"key":"644_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.patcog.2015.08.012","volume":"50","author":"F Wu","year":"2016","unstructured":"Wu F, Jing XY, You XG, Yue D, Hu RM, Yang JY (2016) Multi-view low-rank dictionary learning for image classification. Pattern Recognit 50:143\u2013154","journal-title":"Pattern Recognit"},{"key":"644_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.patcog.2015.11.016","volume":"53","author":"X Chen","year":"2016","unstructured":"Chen X, Xu JM (2016) Uncooperative gait recognition: re-ranking based on sparse coding and multi-view hypergraph learning. Pattern Recognit 53:116\u2013129","journal-title":"Pattern Recognit"},{"issue":"12","key":"644_CR3","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1162\/0899766042321814","volume":"16","author":"DR Hardoon","year":"2004","unstructured":"Hardoon DR, Szedmak S, Taylor JS (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639\u20132664","journal-title":"Neural Comput"},{"key":"644_CR4","first-page":"312","volume":"28","author":"H Hostelling","year":"1936","unstructured":"Hostelling H (1936) Relations between two sets of variables. Biometrika 28:312\u2013377","journal-title":"Biometrika"},{"key":"644_CR5","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.sigpro.2016.03.024","volume":"128","author":"S Yang","year":"2016","unstructured":"Yang S, Schreier PJ, Ramirez D, Hasija T (2016) Canonical correlation analysis of high-dimensional data with very small sample support. Signal Process 128:449\u2013458","journal-title":"Signal Process"},{"key":"644_CR6","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2016.05.001","volume":"205","author":"X Zhang","year":"2016","unstructured":"Zhang X, Liao SZ (2016) Tensor completion via multi-shared-modes canonical correlation analysis. Neurocomputing 205:106\u2013115","journal-title":"Neurocomputing"},{"key":"644_CR7","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.neucom.2015.12.039","volume":"182","author":"J Cai","year":"2016","unstructured":"Cai J, Tang Y, Wang JJ (2016) Kernel canonical correlation analysis via gradient descent. Neurocomputing 182:322\u2013331","journal-title":"Neurocomputing"},{"key":"644_CR8","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patcog.2015.08.011","volume":"50","author":"XL Xing","year":"2016","unstructured":"Xing XL, Wang KJ, Yan T, Lv ZW (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit 50:107\u2013117","journal-title":"Pattern Recognit"},{"key":"644_CR9","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.jprocont.2016.02.006","volume":"41","author":"ZW Chen","year":"2016","unstructured":"Chen ZW, Zhang K, Ding SX, Shardt YAW, Hu ZK (2016) Improved canonical correlation analysis-based fault detection methods for industrial processes. J Process Control 41:26\u201334","journal-title":"J Process Control"},{"key":"644_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.conengprac.2015.10.006","volume":"46","author":"ZW Chen","year":"2016","unstructured":"Chen ZW, Ding SX, Zhang K, Li ZB, Hu ZK (2016) Canonical correlation analysis-based fault detection methods with application to alumina evaporation process. Control Eng Pract 46:51\u201358","journal-title":"Control Eng Pract"},{"key":"644_CR11","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.csda.2016.03.001","volume":"101","author":"LX Zhou","year":"2016","unstructured":"Zhou LX, Takane Y, Hwang HS (2016) Dynamic GSCANO (generalized structured canonical correlation analysis) with applications to the analysis of effective connectivity in functional neuroimaging data. Comput Stat Data Anal 101:93\u2013109","journal-title":"Comput Stat Data Anal"},{"key":"644_CR12","doi-asserted-by":"crossref","unstructured":"Sun TK, Chen SC, Yang, JY, Shi PF (2008) A novel method of combined feature extraction for recognition. In: 8th IEEE international conference on data mining, pp 1043\u20131048","DOI":"10.1109\/ICDM.2008.28"},{"issue":"2","key":"644_CR13","first-page":"263","volume":"40","author":"S Akaho","year":"2007","unstructured":"Akaho S (2007) A kernel method for canonical correlation analysis. Proc Int Meet Psychom Soc 40(2):263\u2013269","journal-title":"Proc Int Meet Psychom Soc"},{"key":"644_CR14","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.imavis.2006.04.014","volume":"25","author":"TK Sun","year":"2007","unstructured":"Sun TK, Chen SC (2007) Locality preserving CCA with applications to data visualization and pose estimation. Image Vis Comput 25:531\u2013543","journal-title":"Image Vis Comput"},{"issue":"1","key":"644_CR15","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1093\/bioinformatics\/btg1045","volume":"19","author":"Y Yamanishi","year":"2003","unstructured":"Yamanishi Y, Vert JP, Nakaya A, Kanehisa M (2003) Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. Bioinformatics 19(1):323\u2013330","journal-title":"Bioinformatics"},{"issue":"8","key":"644_CR16","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.1016\/j.patcog.2012.02.007","volume":"45","author":"XF Zhu","year":"2012","unstructured":"Zhu XF, Huang Z, Shen HT, Cheng J, Xu CS (2012) Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis. Pattern Recognit 45(8):3003\u20133016","journal-title":"Pattern Recognit"},{"issue":"9","key":"644_CR17","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1016\/S0031-3203(03)00058-X","volume":"36","author":"T Melzer","year":"2003","unstructured":"Melzer T, Reiter M, Bischof H (2003) Appearance models based on kernel canonical correlation analysis. Pattern Recognit 36(9):1961\u20131971","journal-title":"Pattern Recognit"},{"issue":"8","key":"644_CR18","first-page":"5120","volume":"63","author":"WF Liu","year":"2016","unstructured":"Liu WF, Zha ZJ, Wang YJ, Lu K, Tao DC (2016) p-Laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron 63(8):5120\u20135129","journal-title":"IEEE Trans Ind Electron"},{"key":"644_CR19","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.sigpro.2014.08.002","volume":"110","author":"WF Liu","year":"2015","unstructured":"Liu WF, Liu HL, Tao DP, Wang YJ, Lu K (2015) Multiview Hessian regularized logistic regression for action recognition. Signal Process 110:101\u2013107","journal-title":"Signal Process"},{"issue":"1","key":"644_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-009-9123-3","volume":"31","author":"Y Peng","year":"2010","unstructured":"Peng Y, Zhang DQ, Zhang JC (2010) A new canonical correlation analysis algorithm with local discriminant. Neural Process Lett 31(1):1\u201315","journal-title":"Neural Process Lett"},{"key":"644_CR21","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.neucom.2016.01.053","volume":"191","author":"C Zu","year":"2016","unstructured":"Zu C, Zhang DQ (2016) Canonical sparse cross-view correlation analysis. Neurocomputing 191:263\u2013272","journal-title":"Neurocomputing"},{"key":"644_CR22","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.neucom.2014.11.066","volume":"154","author":"CM Zhu","year":"2015","unstructured":"Zhu CM, Wang Z, Gao DQ (2015) Globalized and localized canonical correlation analysis with multiple empirical kernel mapping. Neurocomputing 154:257\u2013275","journal-title":"Neurocomputing"},{"key":"644_CR23","unstructured":"Blake CL, Newman DJ, Hettich S, Merz CJ (2012) UCI repository of machine learning databases. [Online]. Available: \n                    http:\/\/archive.ics.uci.edu\/ml\/datasets"},{"key":"644_CR24","unstructured":"Ahonen T, Hadid A, Pietik\n                    \n                      \n                    \n                    $$\\ddot{a}$$\n                    \n                      \n                        \n                          a\n                          \u00a8\n                        \n                      \n                    \n                  inen M (2014) Face recognition with local binary patterns. In: Proceedings of the European conference on computer vision, pp 469\u2013481"},{"key":"644_CR25","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"J Tenenbaum","year":"2000","unstructured":"Tenenbaum J, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319\u20132323","journal-title":"Science"},{"key":"644_CR26","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley GC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079\u20132107","journal-title":"J Mach Learn Res"},{"key":"644_CR27","doi-asserted-by":"publisher","first-page":"3208","DOI":"10.1016\/j.patcog.2013.06.007","volume":"46","author":"Y Zhou","year":"2013","unstructured":"Zhou Y, Liu K, Carrillo RE, Barner KE, Kiamilev F (2013) Kernel-based sparse representation for gesture recognition. Pattern Recognit 46:3208\u20133222","journal-title":"Pattern Recognit"},{"issue":"1","key":"644_CR28","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s10994-005-3561-6","volume":"61","author":"JP Ye","year":"2005","unstructured":"Ye JP (2005) Generalized low rank approximations of matrices. Mach Learn 61(1):167\u2013191","journal-title":"Mach Learn"},{"key":"644_CR29","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.patrec.2017.01.014","volume":"88","author":"CM Zhu","year":"2017","unstructured":"Zhu CM, Wang Z (2017) Entropy-based matrix learning machine for imbalanced data sets. Pattern Recognit Lett 88:72\u201380","journal-title":"Pattern Recognit Lett"},{"key":"644_CR30","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/A:1013999503812","volume":"48","author":"P Bartlett","year":"2002","unstructured":"Bartlett P, Boucheron S, Lugosi G (2002) Model selection and error estimation. Mach Learn 48:85\u2013113","journal-title":"Mach Learn"},{"issue":"5","key":"644_CR31","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1109\/18.930926","volume":"47","author":"V Koltchinskii","year":"2001","unstructured":"Koltchinskii V (2001) Rademacher penalties and structural risk minimization. IEEE Trans Inf Theor 47(5):1902\u20131914","journal-title":"IEEE Trans Inf Theor"},{"key":"644_CR32","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-1-4612-1358-1_29","volume":"II","author":"V Koltchinskii","year":"2000","unstructured":"Koltchinskii V, Panchenko D (2000) Rademacher processes and bounding the risk of function learning. High Dimens Probab II:443\u2013459","journal-title":"High Dimens Probab"},{"issue":"1","key":"644_CR33","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/18.971753","volume":"48","author":"S Mendelson","year":"2002","unstructured":"Mendelson S (2002) Rademacher averages and phase transitions in glivenko\u2013cantelli classes. IEEE Trans Inf Theor 48(1):251\u2013263","journal-title":"IEEE Trans Inf Theor"},{"key":"644_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.knosys.2014.04.012","volume":"65","author":"Z Wang","year":"2015","unstructured":"Wang Z, Zhu CM, Niu ZX, Gao DQ, Feng X (2015) Multi-kernel classification machine with reduced complexity. Knowl Based Syst 65:83\u201395","journal-title":"Knowl Based Syst"},{"key":"644_CR35","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1016\/j.patcog.2014.10.029","volume":"48","author":"CM Zhu","year":"2015","unstructured":"Zhu CM, Gao DQ (2015) Improved multi-kernel classification machine with Nystrom approximation technique. Pattern Recognit 48:1490\u20131509","journal-title":"Pattern Recognit"},{"issue":"2","key":"644_CR36","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1137\/1116025","volume":"16","author":"V Vapnik","year":"1971","unstructured":"Vapnik V, Chervonenkis A (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theor Probab Appl 16(2):264\u2013280","journal-title":"Theor Probab Appl"},{"issue":"5","key":"644_CR37","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1109\/18.930926","volume":"47","author":"V Koltchinskii","year":"2001","unstructured":"Koltchinskii V (2001) Rademacher penalties and structural risk minimization. IEEE Trans Inf Theor 47(5):1902\u20131914","journal-title":"IEEE Trans Inf Theor"},{"key":"644_CR38","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.neucom.2012.05.027","volume":"97","author":"Z Wang","year":"2012","unstructured":"Wang Z, Xu J, Chen SC, Gao DQ (2012) Regularized multi-view machine based on response surface technique. Neurocomputing 97:201\u2013213","journal-title":"Neurocomputing"},{"key":"644_CR39","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.ins.2017.01.011","volume":"385\u2013386","author":"XH Yang","year":"2017","unstructured":"Yang XH, Liu WF, Tao DP, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385\u2013386:338\u2013352","journal-title":"Inf Sci"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-017-0644-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-017-0644-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-017-0644-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T08:52:35Z","timestamp":1557478355000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-017-0644-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,14]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,5]]}},"alternative-id":["644"],"URL":"https:\/\/doi.org\/10.1007\/s10044-017-0644-5","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,14]]},"assertion":[{"value":"17 November 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}