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Surv."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis, including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. 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Process. Syst. 73--80. C. Archambeau and F. R. Bach. 2009. Sparse probabilistic projections. In Adv. Neural Info. Process. Syst. 73--80."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143849"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS054"},{"key":"e_1_2_1_8_1","volume-title":"Obozinski et al","author":"Bach F.","year":"2011","unstructured":"F. Bach , R. Jenatton , J. Mairal , G. Obozinski et al . 2011 . Convex optimization with sparsity-inducing norms. Optim. Mach. Learn . 5 (2011). F. Bach, R. Jenatton, J. Mairal, G. Obozinski et al. 2011. Convex optimization with sparsity-inducing norms. Optim. Mach. Learn. 5 (2011)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244303768966085"},{"key":"e_1_2_1_10_1","unstructured":"F. R. Bach and M. I. Jordan. 2005. A probabilistic interpretation of canonical correlation analysis. (2005). F. R. Bach and M. I. Jordan. 2005. 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Semi-supervised Laplacian regularization of kernel canonical correlation analysis. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 133--145."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1006\/jmps.1999.1279"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-8317.1983.tb00765.x"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2012.10.008"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2015.05.014"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1037\/e473742008-115"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML\u201913)","author":"Chang B.","unstructured":"B. Chang , U. Kr\u00fcger , R. Kustra , and J. Zhang . 2013. Canonical correlation analysis based on Hilbert-Schmidt independence criterion and centered kernel target alignment . 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Removal of muscle artifacts from single-channel EEG based on ensemble empirical mode decomposition and multiset canonical correlation analysis. J. Appl. Math. vol. 2014. Article ID 261347, 10 pages.","journal-title":"J. Appl. Math."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics. 199--207","author":"Chen X.","unstructured":"X. Chen , H. Liu , and J. G. Carbonell . 2012. Structured sparse canonical correlation analysis . In Proceedings of the International Conference on Artificial Intelligence and Statistics. 199--207 . X. Chen, H. Liu, and J. G. Carbonell. 2012. Structured sparse canonical correlation analysis. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 199--207."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw052"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013625426931"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2013.09.020"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0024-3795(96)00244-3"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476626"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1080\/00220973.1975.10806349"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02288367"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/1021092"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the 32nd Annual Technical Symposium. International Society for Optics and Photonics, 206--222","author":"Ewerbring L. 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IEEE, 1343--1348","author":"Heij C.","unstructured":"C. Heij and B. Roorda . 1991. A modified canonical correlation approach to approximate state space modelling . In Proceedings of the 30th IEEE Conference on Decision and Control. IEEE, 1343--1348 . C. Heij and B. Roorda. 1991. A modified canonical correlation approach to approximate state space modelling. In Proceedings of the 30th IEEE Conference on Decision and Control. IEEE, 1343--1348."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.2307\/1271436"},{"key":"e_1_2_1_49_1","doi-asserted-by":"crossref","unstructured":"J. W. Hooper. 1959. Simultaneous equations and canonical correlation theory. Econometr.: J. Econometr. Soc. (1959) 245--256. J. W. Hooper. 1959. Simultaneous equations and canonical correlation theory. Econometr.: J. Econometr. Soc. (1959) 245--256.","DOI":"10.2307\/1909445"},{"key":"e_1_2_1_50_1","first-page":"304","article-title":"Statistical analysis by canonical correlation: A computer application.Health","volume":"4","author":"Hopkins C. E.","year":"1969","unstructured":"C. E. Hopkins . 1969 . Statistical analysis by canonical correlation: A computer application.Health Serv. Res. 4 , 4 (1969), 304 . C. E. Hopkins. 1969. Statistical analysis by canonical correlation: A computer application.Health Serv. Res. 4, 4 (1969), 304.","journal-title":"Serv. 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In Proceedings of the 2013 35th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC\u201913) . IEEE, 1490--1493. M. Kang, B. Zhang, X. Wu, C. Liu, and J. Gao. 2013. Sparse generalized canonical correlation analysis for biological model integration: A genetic study of psychiatric disorders. In Proceedings of the 2013 35th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC\u201913). IEEE, 1490--1493."},{"key":"e_1_2_1_58_1","volume-title":"Canonical analysis of several sets of variables. Biometrika","author":"Kettenring J. R.","year":"1971","unstructured":"J. R. Kettenring . 1971. Canonical analysis of several sets of variables. Biometrika ( 1971 ), 433--451. J. R. Kettenring. 1971. Canonical analysis of several sets of variables. Biometrika (1971), 433--451."},{"key":"e_1_2_1_59_1","first-page":"311","article-title":"SemiCCA: Efficient semi-supervised learning of canonical correlations. Info","volume":"8","author":"Kimura A.","year":"2013","unstructured":"A. Kimura , M. Sugiyama , T. Nakano , H. Kameoka , H. Sakano , E. Maeda , and K. Ishiguro . 2013 . SemiCCA: Efficient semi-supervised learning of canonical correlations. Info . Media Technol. 8 , 2 (2013), 311 -- 318 . A. Kimura, M. Sugiyama, T. Nakano, H. Kameoka, H. Sakano, E. Maeda, and K. Ishiguro. 2013. SemiCCA: Efficient semi-supervised learning of canonical correlations. Info. Media Technol. 8, 2 (2013), 311--318.","journal-title":"Media Technol."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273550"},{"key":"e_1_2_1_61_1","unstructured":"A. Klami S. Virtanen and S. Kaski. 2012. Bayesian exponential family projections for coupled data sources. arXiv:1203.3489 (2012). A. Klami S. Virtanen and S. Kaski. 2012. Bayesian exponential family projections for coupled data sources. arXiv:1203.3489 (2012)."},{"key":"e_1_2_1_62_1","first-page":"965","article-title":"Bayesian canonical correlation analysis","author":"Klami A.","year":"2013","unstructured":"A. Klami , S. Virtanen , and S. Kaski . 2013 . Bayesian canonical correlation analysis . J. Mach. Learn. Res. 14 , Apr (2013), 965 -- 1003 . A. Klami, S. Virtanen, and S. Kaski. 2013. Bayesian canonical correlation analysis. J. Mach. Learn. Res. 14, Apr (2013), 965--1003.","journal-title":"J. Mach. Learn. 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Moyeed and B. W. Silverman. 1993. Canonical correlation analysis when the data are curves. J. Roy. Stat. Soc. Ser. B (Methodol.) (1993) 725--740. S. E. Leurgans R. A. Moyeed and B. W. Silverman. 1993. Canonical correlation analysis when the data are curves. J. Roy. Stat. Soc. Ser. B (Methodol.) (1993) 725--740.","DOI":"10.1111\/j.2517-6161.1993.tb01936.x"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.4710270303"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1515\/sagmb-2012-0032"},{"key":"e_1_2_1_72_1","volume-title":"Proceedings of the International Conference on Artificial Neural Networks. Springer, 353--360","author":"Melzer T.","unstructured":"T. Melzer , M. Reiter , and H. Bischof . 2001. Nonlinear feature extraction using generalized canonical correlation analysis . In Proceedings of the International Conference on Artificial Neural Networks. Springer, 353--360 . T. Melzer, M. Reiter, and H. Bischof. 2001. Nonlinear feature extraction using generalized canonical correlation analysis. In Proceedings of the International Conference on Artificial Neural Networks. Springer, 353--360."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(03)00058-X"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289567"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0033-0124.1973.00140.x"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140703"},{"key":"e_1_2_1_77_1","volume-title":"Bayesian Learning for Neural Networks","author":"Neal R. M.","unstructured":"R. M. Neal . 2012. Bayesian Learning for Neural Networks . Vol. 118 . Springer Science 8 Business Media. R. M. Neal. 2012. Bayesian Learning for Neural Networks. Vol. 118. 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In Advances in Neural Information Processing Systems. 1518--1526."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003018"},{"key":"e_1_2_1_82_1","volume-title":"Numerical Methods for Large Eigenvalue Problems","author":"Saad Y.","unstructured":"Y. Saad . 2011. Numerical Methods for Large Eigenvalue Problems . Vol. 158 . SIAM. Y. Saad. 2011. Numerical Methods for Large Eigenvalue Problems. Vol. 158. SIAM."},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2008.09.005"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12038-015-9555-z"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.476433"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003876"},{"key":"e_1_2_1_88_1","doi-asserted-by":"crossref","unstructured":"J. Shawe-Taylor and N. Cristianini. 2004. 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IEEE, 151--157."},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-191"},{"key":"e_1_2_1_91_1","volume-title":"Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910)","author":"Song L.","unstructured":"L. Song , B. Boots , S. M. Siddiqi , G. J. Gordon , and A. Smola . 2010. Hilbert space embeddings of hidden Markov models . In Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910) . Johannes F\u00fcrnkranz and Thorsten Joachims (eds). Omnipress, USA, 991\u2013998. L. Song, B. Boots, S. M. Siddiqi, G. J. Gordon, and A. Smola. 2010. Hilbert space embeddings of hidden Markov models. In Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910). Johannes F\u00fcrnkranz and Thorsten Joachims (eds). Omnipress, USA, 991\u2013998."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.05.020"},{"key":"e_1_2_1_93_1","volume-title":"Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.)","author":"Stone M.","year":"1974","unstructured":"M. Stone . 1974. Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.) ( 1974 ), 111--147. M. Stone. 1974. Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.) (1974), 111--147."},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1529-8817.1982.tb03166.x"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.5555\/2793730.2794013"},{"key":"e_1_2_1_96_1","doi-asserted-by":"crossref","unstructured":"A. Tenenhaus C. Philippe V. Guillemot K.-A. Le Cao J. Grill and V. Frouin. 2014. Variable selection for generalized canonical correlation analysis. 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Springer, 299--307."},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02294207"},{"key":"e_1_2_1_104_1","volume-title":"Proceedings of the International Conference on Artificial Neural Networks. Springer, 384--389","author":"Van Gestel T.","unstructured":"T. Van Gestel , J. A. K. Suykens , J. De Brabanter , B. De Moor , and J. Vandewalle . 2001. Kernel canonical correlation analysis and least-squares support vector machines . In Proceedings of the International Conference on Artificial Neural Networks. Springer, 384--389 . T. Van Gestel, J. A. K. Suykens, J. De Brabanter, B. De Moor, and J. Vandewalle. 2001. Kernel canonical correlation analysis and least-squares support vector machines. In Proceedings of the International Conference on Artificial Neural Networks. Springer, 384--389."},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(76)90010-5"},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1329"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.891186"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2005.1615832"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.4310\/SII.2013.v6.n2.a3"},{"key":"e_1_2_1_110_1","volume-title":"Fundamentals of Matrix Computations","author":"Watkins D. S.","unstructured":"D. S. Watkins . 2004. Fundamentals of Matrix Computations . Vol. 64 . John Wiley 8 Sons. D. S. Watkins. 2004. Fundamentals of Matrix Computations. Vol. 64. John Wiley 8 Sons."},{"key":"e_1_2_1_111_1","doi-asserted-by":"crossref","unstructured":"F. V. Waugh. 1942. Regressions between sets of variables. Econometr. J. Econometr. Soc. (1942) 290--310. F. V. Waugh. 1942. Regressions between sets of variables. 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Semi-paired probabilistic canonical correlation analysis. In Proceedings of the International Conference on Intelligent Information Processing. 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