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Relevant bibliographies by topics / Canonical correlation

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Academic literature on the topic 'Canonical correlation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Canonical correlation"

1

Mazuruse, Peter. "Canonical correlation analysis." Journal of Financial Economic Policy 6, no. 2 (2014): 179–96. http://dx.doi.org/10.1108/jfep-09-2013-0047.

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Purpose – The purpose of this paper was to construct a canonical correlation analysis (CCA) model for the Zimbabwe stock exchange (ZSE). This paper analyses the impact of macroeconomic variables on stock returns for the Zimbabwe Stock Exchange using the canonical correlation analysis (CCA). Design/methodology/approach – Data for the independent (macroeconomic) variables and dependent variables (stock returns) were extracted from secondary sources for the period from January 1990 to December 2008. For each variable, 132 sets of data were collected. Eight top trading companies at the ZSE were se

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Cope, Leslie, Daniel Q. Naiman, and Giovanni Parmigiani. "Integrative correlation: Properties and relation to canonical correlations." Journal of Multivariate Analysis 123 (January 2014): 270–80. http://dx.doi.org/10.1016/j.jmva.2013.09.011.

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Lipovetsky, Stan. "Canonical Concordance Correlation Analysis." Mathematics 11, no. 1 (2022): 99. http://dx.doi.org/10.3390/math11010099.

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A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical Canonical Correlation Analysis (CCA) which is based on maximization of the Pearson’s correlation coefficient between the linear combinations of two sets of variables, the CCCA maximizes the Lin’s concordance correlation coefficient which accounts not just for the maximum correlation but also for the closeness of the aggregates’ mean values and the closeness of their variances. While the CCA employs the centered data with excluded means of the variables, the CCCA can be u

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Huang, Qing, and Rosemary Renaut. "Functional partial canonical correlation." Bernoulli 21, no. 2 (2015): 1047–66. http://dx.doi.org/10.3150/14-bej597.

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Lipovetsky, Stan. "Orthonormal Canonical Correlation Analysis." Open Statistics 2, no. 1 (2021): 24–36. http://dx.doi.org/10.1515/stat-2020-0104.

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Abstract Complex managerial problems are usually described by datasets with multiple variables, and in lack of a theoretical model, the data structures can be found by special multivariate statistical techniques. For two datasets, the canonical correlation analysis and its robust version are known as good working research tools. This paper presents their further development via the orthonormal approximation of data matrices which corresponds to using singular value decomposition in the canonical correlations. The features of the new method are described and applications considered. This type o

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Zhao, Hongmin, Dongting Sun, and Zhigang Luo. "Incremental Canonical Correlation Analysis." Applied Sciences 10, no. 21 (2020): 7827. http://dx.doi.org/10.3390/app10217827.

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Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance m

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Guo, Yiwen, Xiaoqing Ding, Changsong Liu, and Jing-Hao Xue. "Sufficient Canonical Correlation Analysis." IEEE Transactions on Image Processing 25, no. 6 (2016): 2610–19. http://dx.doi.org/10.1109/tip.2016.2551374.

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Cocozzelli, Carmelo. "Understanding Canonical Correlation Analysis." Journal of Social Service Research 13, no. 4 (1990): 19–42. http://dx.doi.org/10.1300/j079v13n04_02.

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Yeom, Ah-Rim, and Yong-Seok Choi. "Partial Canonical Correlation Biplot." Korean Journal of Applied Statistics 24, no. 3 (2011): 559–66. http://dx.doi.org/10.5351/kjas.2011.24.3.559.

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Sakar, C. Okan, Olcay Kursun, and Fikret Gurgen. "Ensemble canonical correlation analysis." Applied Intelligence 40, no. 2 (2013): 291–304. http://dx.doi.org/10.1007/s10489-013-0464-2.

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Dissertations / Theses on the topic "Canonical correlation"

1

Lai, Pei Ling. "Neural implementations of canonical correlation analysis." Thesis, University of the West of Scotland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311771.

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Hadjiyiannis, Nicholas. "Canonical correlation of shipping forward curves." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62532.

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Thesis (S.M. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 106-109).<br>The behavior and interrelations between the main shipping forward curves are analyzed using multivariate statistics after removing the volatility distortions dictated by the Samuelson hypothesis. Principal Components Analysis and Canonical Correlation analysis were used to demonstrate how the task of explaining the various shipping forward curves can be simplified substantially and how

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Gou, Zhenkun. "Canonical correlation analysis and artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269409.

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Lykou, Anastasia. "Sparse canonical correlation analysis using the Lasso." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533099.

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Cai, Jia. "Learning gradients and canonical correlation by kernel methods /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b23749349f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [52]-58)

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Leach, Lesley Ann Freeny. "Bias and Precision of the Squared Canonical Correlation Coefficient under Nonnormal Data Conditions." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5361/.

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This dissertation: (a) investigated the degree to which the squared canonical correlation coefficient is biased in multivariate nonnormal distributions and (b) identified formulae that adjust the squared canonical correlation coefficient (Rc2) such that it most closely approximates the true population effect under normal and nonnormal data conditions. Five conditions were manipulated in a fully-crossed design to determine the degree of bias associated with Rc2: distribution shape, variable sets, sample size to variable ratios, and within- and between-set correlations. Very few of the conditi

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Gu, Hong, and 谷紅. "Influence diagnostics in principal components and canonical analyses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31238518.

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Wilson, Celia M. "Attenuation of the Squared Canonical Correlation Coefficient Under Varying Estimates of Score Reliability." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30528/.

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Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability. Monte Carlo simulation methodology was used to fulfill the purpose of this study. Initially, data populations with various manipulated conditions were generated (N = 100,000). Subsequently, 500 random samples were drawn with replacement from each pop

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Samarov, Daniel V. Marron James Stephen. "The analysis and advanced extensions of canonical correlation analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2205.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.<br>Title from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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Yamazaki, Ryo. "Random Subspace Analysis on Canonical Correlation of High Dimensional Data." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412.

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High dimensional, low sample, data have singular sample covariance matrices,rendering them impossible to analyse by regular canonical correlation (CC). Byusing random subspace method (RSM) calculation of canonical correlation be-comes possible, and a Monte Carlo analysis shows resulting maximal CC canreliably distinguish between data with true correlation (above 0.5) and with-out. Statistics gathered from RSMCCA can be used to model true populationcorrelation by beta regression, given certain characteristic of data set. RSM-CCA applied on real biological data however show that the method can b

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Books on the topic "Canonical correlation"

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Burg, Eeke Van der. Nonlinear canonical correlation and some related techniques. DSWO Press, 1988.

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Benesty, Jacob, and Israel Cohen. Canonical Correlation Analysis in Speech Enhancement. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67020-1.

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Pourahmadi, Mohsen. Computation of canonical correlation and best predictable aspect of future for time series. National Aeronautics and Space Administration, 1989.

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Bulach, Marcia Woolf. Canonical Auto And Cross Correlations Of Multivariate Time Series. Dissertation.com, 1999.

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Röhr, Michael. Statistische Strukturanalysen. G. Fischer, 1993.

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Theodoulidis, Theodoros P. Eddy current canonical problems (with applications to nondestructive evaluation). Tech Science Press, 2006.

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Orlóci, László. Conapack: Program for canonical analysis of classification tables. SPB Academic, 1992.

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8

Petz, Dénes. An invitation to the algebra of canonical commutation relations. Leuven University Press, 1990.

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9

Anderson, Richard G. Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators. Federal Reserve Bank of St. Louis, 2006.

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Israel, Cohen, and Jacob Benesty. Canonical Correlation Analysis in Speech Enhancement. Springer, 2017.

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Book chapters on the topic "Canonical correlation"

1

Hooker, Stephanie Ann. "Canonical Correlation." In Encyclopedia of Behavioral Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1579.

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Hooker, Stephanie Ann. "Canonical Correlation." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1579.

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Brown, Charles E. "Canonical Correlation." In Applied Multivariate Statistics in Geohydrology and Related Sciences. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80328-4_5.

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Kullback, Solomon, John C. Keegel, and Joseph H. Kullback. "Canonical Correlation." In Topics in Statistical Information Theory. Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4615-8080-5_6.

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Gatignon, Hubert. "Canonical Correlation Analysis." In Statistical Analysis of Management Data. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-8594-0_7.

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Forsyth, David. "Canonical Correlation Analysis." In Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_7.

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Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Canonical Correlation Analysis." In Multivariate Statistics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_16.

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Härdle, Wolfgang, and Léopold Simar. "Canonical Correlation Analysis." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_14.

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Härdle, Wolfgang Karl, and Léopold Simar. "Canonical Correlation Analysis." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_16.

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Härdle, Wolfgang Karl, and Léopold Simar. "Canonical Correlation Analysis." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_15.

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Conference papers on the topic "Canonical correlation"

1

Tang, Shiqin, Shujian Yu, Yining Dong, and S. Joe Qin. "Deep Dynamic Probabilistic Canonical Correlation Analysis." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887585.

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Malhotra, Akshay, Ioannis D. Schizas, and João Morais. "Deep Linearization Mappings for Canonical Correlation Clustering." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10943012.

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Kalantzis, Vassilis, Panagiotis A. Traganitis, and Charilaos I. Kanatsoulis. "Online Canonical Correlation Analysis via Rayleigh-Ritz Projections." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942937.

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Chen, Shangyu, Shuo Wang, and Richard Sinnott. "Parametric Canonical Correlation Analysis." In 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2019. http://dx.doi.org/10.1109/cloudcom.2019.00060.

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Zhang, Jianchun, and Daoqiang Zhang. "Canonical random correlation analysis." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774321.

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Asendorf, Nicholas, and Raj Rao Nadakuditi. "Improved estimation of canonical vectors in canonical correlation analysis." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421463.

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Ting-Kai Sun, Song-Can Chen, Zhong Jin, and Jing-Yu Yang. "Kernelized discriminative canonical correlation analysis." In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421632.

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Benton, Adrian, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, and Raman Arora. "Deep Generalized Canonical Correlation Analysis." In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-4301.

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Imtiaz, Hafiz, and Anand D. Sarwate. "Differentially-private canonical correlation analysis." In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8308649.

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Kamada, Chie, Asako Kanezaki, and Tatsuya Harada. "Probabilistic Semi-Canonical Correlation Analysis." In MM '15: ACM Multimedia Conference. ACM, 2015. http://dx.doi.org/10.1145/2733373.2806299.

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Reports on the topic "Canonical correlation"

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Todros, Koby, and Alfred O. Hero. On Measure Transformed Canonical Correlation Analysis. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada578246.

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Krishnaiah, P. R., J. Lin, and L. Wang. Inference on the Ranks of the Canonical Correlation Matrices for Elliptically Symmetric Populations. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada158268.

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Fujikoshi, Y., P. R. Krishnaiah, and J. Schmidhammer. Effect of Additional Variables in Principal Component Analysis, Discriminant Analysis and Canonical Correlation Analysis. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162069.

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Anderson, Richard G., Hailong Qian, and Robert H. Rasche. Analysis of Panel Vector Error Correction Models Using Maximum Likelihood, the Bootstrap, and Canonical Correlation Estimators. Federal Reserve Bank of St. Louis, 2006. http://dx.doi.org/10.20955/wp.2006.050.

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Nishii, R., Z. D. Bai, and P. R. Krishnaiah. Strong Consistency of Certain Information Theoretic Criteria for Model Selection in Calibration, Discriminant Analysis and Canonical Correlation Analysis. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada186584.

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Taniguchi, M., and P. R. Krishnaiah. Asymptotic Distributions of Functions of the Eigenvalues of the Sample Covariance Matrix and Canonical Correlation Matrix in Multivariate Time Series. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170282.

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Lee, Jhong S., Leonard E. Miller, Robert H. French, and Young K. Kim. Ocean Surveillance Detection Studies. Part 1. Detection in Gaussian Mixture Noise. Part 2. An Investigation of Canonical Correlation as an Automatic Detection and Beamforming Technique. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160931.

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Irving, William W., and Alan S. Willsky. A Canonical Correlations Approach to Multiscale Stochastic Realization. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada459475.

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Systematic rare plant survey at the Cuyahoga Valley National Park: Final report (public version). National Park Service, 2023. http://dx.doi.org/10.36967/2300360.

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EnviroScience completed a systematic rare plant survey in the Cuyahoga Valley National Park (CUVA). CUVA staff use the visitor use management planning (VUMP) process to draft proposed management zones for the park to track resource degradation and guide management decisions. The VUMP process has identified a critical need for a park-wide rare plant inventory to supplement outdated surveys conducted from 1986 to the early 2000s. This systematic transect-based survey was designed to provide current rare plant data to inform the VUMP process. These updated rare plant data will support the Nationa

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Systematic rare plant survey at the Cuyahoga Valley National Park: Final report (sensitive version). National Park Service, 2023. http://dx.doi.org/10.36967/2300521.

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EnviroScience, Inc. completed a systematic rare plant survey in the Cuyahoga Valley National Park (CUVA). CUVA staff use the visitor use management planning (VUMP) process to draft proposed management zones for the park to track resource degradation and guide management decisions. The VUMP process has identified a critical need for a park-wide rare plant inventory to supplement outdated surveys conducted from 1986 to the early 2000s. This systematic transect-based survey was designed to provide current rare plant data to inform the VUMP process. These updated rare plant data will support the N

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