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Journal articles on the topic 'Canonical correlation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Hardoon, David R., and John Shawe-Taylor. "Sparse canonical correlation analysis." Machine Learning 83, no. 3 (2010): 331–53. http://dx.doi.org/10.1007/s10994-010-5222-7.

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12

Krzyśko, Mirosław, and Łukasz Waszak. "Canonical correlation analysis for functional data." Biometrical Letters 50, no. 2 (2013): 95–105. http://dx.doi.org/10.2478/bile-2013-0020.

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Summary Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we propose a new method of constructing canonical correlations and canonical variables for a pair of stochastic processes represented by a finite number of orthonormal basi

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13

Savicevic, Dusan. "Canonical relations between intelligence and aggressiveness." Psihologija 35, no. 1-2 (2002): 97–113. http://dx.doi.org/10.2298/psi0201097s.

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Relation between results obtained from 25 tests for estimation of efficiency of perceptual, serial and parallel processor and 8 tests that estimate different modalities of aggressiveness, were analyzed under the biorthogonal model of canonical correlation analysis, on a sample of 647 male subjects, age between 19 to 27. The results show that there is significant, medium high (0,65), logically negative canonical correlation between cognitive efficiency and aggressiveness. Canonical factor derived from cognitive tests was similar to general cognitive factor because all cognitive tests had substa

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14

Teng, Zhongming, and Xiaowei Zhang. "A Jacobi–Davidson Method for Large Scale Canonical Correlation Analysis." Algorithms 13, no. 9 (2020): 229. http://dx.doi.org/10.3390/a13090229.

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In the large scale canonical correlation analysis arising from multi-view learning applications, one needs to compute canonical weight vectors corresponding to a few of largest canonical correlations. For such a task, we propose a Jacobi–Davidson type algorithm to calculate canonical weight vectors by transforming it into the so-called canonical correlation generalized eigenvalue problem. Convergence results are established and reveal the accuracy of the approximate canonical weight vectors. Numerical examples are presented to support the effectiveness of the proposed method.

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15

Akaho, Shotaro. "Introduction to Canonical Correlation Analysis." Brain & Neural Networks 20, no. 2 (2013): 62–72. http://dx.doi.org/10.3902/jnns.20.62.

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16

Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.

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We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of pr

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17

Lee, Bo-Hui, Yong-Seok Choi, and Sang-Min Shin. "Semi-Partial Canonical Correlation Biplot." Korean Journal of Applied Statistics 25, no. 3 (2012): 521–29. http://dx.doi.org/10.5351/kjas.2012.25.3.521.

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18

Takane, Yoshio, and Heungsun Hwang. "Generalized Constrained Canonical Correlation Analysis." Multivariate Behavioral Research 37, no. 2 (2002): 163–95. http://dx.doi.org/10.1207/s15327906mbr3702_01.

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19

Eubank, R. L., and Tailen Hsing. "Canonical correlation for stochastic processes." Stochastic Processes and their Applications 118, no. 9 (2008): 1634–61. http://dx.doi.org/10.1016/j.spa.2007.10.006.

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20

Otopal, Nina. "Restricted kernel canonical correlation analysis." Linear Algebra and its Applications 437, no. 1 (2012): 1–13. http://dx.doi.org/10.1016/j.laa.2012.02.014.

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21

Chen, Jia, Gang Wang, and Georgios B. Giannakis. "Graph Multiview Canonical Correlation Analysis." IEEE Transactions on Signal Processing 67, no. 11 (2019): 2826–38. http://dx.doi.org/10.1109/tsp.2019.2910475.

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22

Kruger, Uwe, and S. Joe Qin. "Canonical Correlation Partial Least Squares." IFAC Proceedings Volumes 36, no. 16 (2003): 1603–8. http://dx.doi.org/10.1016/s1474-6670(17)34989-3.

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23

Zhai, Deming, Yu Zhang, Dit-Yan Yeung, Hong Chang, Xilin Chen, and Wen Gao. "Instance-specific canonical correlation analysis." Neurocomputing 155 (May 2015): 205–18. http://dx.doi.org/10.1016/j.neucom.2014.12.028.

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24

Romanazzi, Mario. "Influence in canonical correlation analysis." Psychometrika 57, no. 2 (1992): 237–59. http://dx.doi.org/10.1007/bf02294507.

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25

Cruz-Cano, Raul, and Mei-Ling Ting Lee. "Fast regularized canonical correlation analysis." Computational Statistics & Data Analysis 70 (February 2014): 88–100. http://dx.doi.org/10.1016/j.csda.2013.09.020.

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26

Tenenhaus, Arthur, Cathy Philippe, and Vincent Frouin. "Kernel Generalized Canonical Correlation Analysis." Computational Statistics & Data Analysis 90 (October 2015): 114–31. http://dx.doi.org/10.1016/j.csda.2015.04.004.

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27

MIN, Wenwen, Juan LIU, and Shihua ZHANG. "Sparse Weighted Canonical Correlation Analysis." Chinese Journal of Electronics 27, no. 3 (2018): 459–66. http://dx.doi.org/10.1049/cje.2017.08.004.

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28

Lee, Sun Ho, and Seungjin Choi. "Two-Dimensional Canonical Correlation Analysis." IEEE Signal Processing Letters 14, no. 10 (2007): 735–38. http://dx.doi.org/10.1109/lsp.2007.896438.

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29

Tenenhaus, Arthur, and Michel Tenenhaus. "Regularized Generalized Canonical Correlation Analysis." Psychometrika 76, no. 2 (2011): 257–84. http://dx.doi.org/10.1007/s11336-011-9206-8.

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30

Song, Chuan-Dong, Jian Li, Yan-Yan Hou, Qing-Hui Liu, and Zhuo Wang. "Quantum canonical correlation analysis algorithm." Laser Physics Letters 20, no. 10 (2023): 105203. http://dx.doi.org/10.1088/1612-202x/acee63.

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Abstract Canonical correlation analysis (CCA) is a fundamental technique used to analyze data correlation in various fields, including video and medical data analysis. In this paper, we propose a quantum canonical correlation analysis (QCCA) algorithm. First, we introduce a combined density matrix representation method that transforms CCA into generalized eigenvalue decomposition. Moreover, to address the challenge of performing generalized eigenvalue decomposition in high-dimensional scenarios, we propose a quantum method for extracting the canonical principal axes. In this method, two sets o

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31

Akbaş, Y., and Ç. Takma. "Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers." Czech Journal of Animal Science 50, No. 4 (2011): 163–68. http://dx.doi.org/10.17221/4010-cjas.

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In this study, canonical correlation analysis was applied to layer data to estimate the relationships of egg production with age at sexual maturity, body weight and egg weight. For this purpose, it was designed to evaluate the relationship between two sets of variables of laying hens: egg numbers at three different periods as the first set of variables (Y) and age at sexual maturity, body weight, egg weight as the second set of variables (X) by using canonical correlation analysis. Estimated canonical correlations between the first and the second pair of canonical variates were significant (P

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32

Qiu, Lin, and Vernon M. Chinchilli. "Probabilistic canonical correlation analysis for sparse count data." Journal of Statistical Research 56, no. 1 (2023): 75–100. http://dx.doi.org/10.3329/jsr.v56i1.63947.

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Canonical correlation analysis (CCA) is a classical and important multivariate technique for exploring the relationship between two sets of continuous variables. CCA has applications in many ﬁelds, such as genomics and neuroimaging. It can extract meaningful features as well as use these features for subsequent analysis. Although some sparse CCA methods have been developed to deal with high-dimensional problems, they are designed speciﬁcally for continuous data and do not consider the integer-valued data from next-generation sequencing platforms that exhibit very low counts for some important

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33

Vicario, A., L. I. Mazón, A. Aguirre, A. Estomba, and C. Lostao. "Relationships between environmental factors and morph polymorphism in Cepaea nemoralis, using canonical correlation analysis." Genome 32, no. 5 (1989): 908–12. http://dx.doi.org/10.1139/g89-528.

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The relationship between phenotype distribution of Cepaea nemoralis and environmental factors was investigated at 105 sites in northern Spain, using canonical correlation analysis. Two interpretable canonical correlations were identified between the phenotype and environmental variable groups at the 0.05 level of significance, and canonical loadings were determined for each set of variables. The first canonical correlation represents the association of unbanded phenotypes with rainy and cloudy sites and of the more banded phenotypes (five banded and fused bands) with high degrees of insolation

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34

Wang, Wenjing, Yuwu Lu, and Zhihui Lai. "Symmetrical Robust Canonical Correlation Analysis for Image Classification." AATCC Journal of Research 8, no. 1_suppl (2021): 54–61. http://dx.doi.org/10.14504/ajr.8.s1.7.

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Canonical correlation analysis (CCA) is a useful technique for multivariate data analysis, which can find correlations between two sets of multidimensional data. CCA projects two sets of data into a low-dimensional space in which the correlations between them are maximized. However, CCA is sensitive to noise or outliers in the collected data of real-world applications, which will degrade its performance. To overcome this disadvantage, we propose symmetrical robust canonical correlation analysis (SRCCA) for image classification. By using low-rank learning, the noise is removed, and CCA is used

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35

Kostić, Milan, and Jelena Živković. "Domestic Competition, Trade Openness and Entrepreneurial Culture: Canonical Correlation Analysis." South East European Journal of Economics and Business 19, no. 1 (2024): 18–31. http://dx.doi.org/10.2478/jeb-2024-0002.

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Abstract The paper analyses canonical correlations between domestic competition, trade openness and entrepreneurial culture. The research covered 141 countries ranked by World Competitiveness Index in 2019. Canonical correlation analysis is applied to find relationship between two canonical variables. The first canonical variable includes sub-indexes from Domestic competition and Trade openness pillars. The second variable contains sub-indexes from Entrepreneurial culture pillar. The results of the analysis showed there is a strong, positive, statistically significant canonical correlation bet

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36

Andrejiova, Miriam, Anna Grincova, and Daniela Marasova. "Comprehensive Evaluation of Conveyor Belt Impact Resistance Using Canonical Correlation Analysis." Applied Sciences 15, no. 5 (2025): 2639. https://doi.org/10.3390/app15052639.

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The aim of the research was to comprehensively evaluate the impact resistance of conveyor belts. Initially, variables were identified that describe the input conditions of the experiment (weight of impacting material, impact height, type and strength of conveyor belt) and subsequent dependent variables that describe the result of the experiment (impact force, increase in tension force, relative amount of absorbed energy, degree of damage). For each dependent variable, its dependence on input variables was monitored through multiple regression analysis. In the next step, through canonical corre

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37

Cunha, Márcio Vieira da, Mario de Andrade Lira, Mércia Virginia Ferreira dos Santos, et al. "Association between the morphological and productive characteristics in the selection of elephant grass clones." Revista Brasileira de Zootecnia 40, no. 3 (2011): 482–88. http://dx.doi.org/10.1590/s1516-35982011000300004.

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The objectives in this work were to study the association between the morphological and productive characteristics in Pennisetum sp. clones, and to identify the morphological characteristics responsible for the productivity in Pennisetum cp. clones. The canonical correlations were evaluated and the path analysis was made from the simple genotypic correlation matrix between the morphological and productive characteristics of eight Pennisetum sp. clones (Taiwan A-146 2.37, Taiwan A-146 2.27, Taiwan-146 2.114, Merker México MX 6.31, Mott, HV-241, Elefante B and IRI-381). The canonical correlation

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38

Tucker, Raymond K. "Multivariate Statistical Models: I. Canonical Correlation." Perceptual and Motor Skills 69, no. 2 (1989): 522. http://dx.doi.org/10.2466/pms.1989.69.2.522.

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39

Zhang, Hongjie, Junyan Tan, Jinxin Zhang, Yingyi Chen, and Ling Jing. "Double information preserving canonical correlation analysis." Engineering Applications of Artificial Intelligence 112 (June 2022): 104870. http://dx.doi.org/10.1016/j.engappai.2022.104870.

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40

SEOK, Jongwon, and Keunsung BAE. "Microphone Classification Using Canonical Correlation Analysis." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 4 (2014): 1024–26. http://dx.doi.org/10.1587/transfun.e97.a.1024.

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41

PENG, Yan, and Dao-Qiang ZHANG. "Semi-Supervised Canonical Correlation Analysis Algorithm." Journal of Software 19, no. 11 (2009): 2822–32. http://dx.doi.org/10.3724/sp.j.1001.2008.02822.

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42

Uurtio, Viivi, João M. Monteiro, Jaz Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, and Juho Rousu. "A Tutorial on Canonical Correlation Methods." ACM Computing Surveys 50, no. 6 (2018): 1–33. http://dx.doi.org/10.1145/3136624.

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43

Todros, Koby, and Alfred O. Hero. "On Measure Transformed Canonical Correlation Analysis." IEEE Transactions on Signal Processing 60, no. 9 (2012): 4570–85. http://dx.doi.org/10.1109/tsp.2012.2203816.

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44

Shen, Cencheng, Ming Sun, Minh Tang, and Carey E. Priebe. "Generalized canonical correlation analysis for classification." Journal of Multivariate Analysis 130 (September 2014): 310–22. http://dx.doi.org/10.1016/j.jmva.2014.05.011.

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45

Zu, Chen, and Daoqiang Zhang. "Canonical sparse cross-view correlation analysis." Neurocomputing 191 (May 2016): 263–72. http://dx.doi.org/10.1016/j.neucom.2016.01.053.

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46

Winkler, Anderson M., Olivier Renaud, Stephen M. Smith, and Thomas E. Nichols. "Permutation inference for canonical correlation analysis." NeuroImage 220 (October 2020): 117065. http://dx.doi.org/10.1016/j.neuroimage.2020.117065.

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47

Pimentel, Harold, Zhiyue Hu, and Haiyan Huang. "Biclustering by sparse canonical correlation analysis." Quantitative Biology 6, no. 1 (2018): 56–67. http://dx.doi.org/10.1007/s40484-017-0127-0.

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48

Yanai, Haruo, and Yoshio Takane. "Canonical correlation analysis with linear constraints." Linear Algebra and its Applications 176 (November 1992): 75–89. http://dx.doi.org/10.1016/0024-3795(92)90211-r.

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49

Sharma, Sanjay K., Uwe Kruger, and George W. Irwin. "Deflation based nonlinear canonical correlation analysis." Chemometrics and Intelligent Laboratory Systems 83, no. 1 (2006): 34–43. http://dx.doi.org/10.1016/j.chemolab.2005.12.008.

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50

Muirhead, Robb J., and Pui Lam Leung. "Estimating functions of canonical correlation coefficients." Linear Algebra and its Applications 70 (October 1985): 173–83. http://dx.doi.org/10.1016/0024-3795(85)90050-3.

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