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Relevant bibliographies by topics / Canonical correlation analysi

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Academic literature on the topic 'Canonical correlation analysi'

Author: Grafiati

Published: 21 February 2023

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Journal articles on the topic "Canonical correlation analysi"

1

Lim, Jun-seok. "An Adaptive Time Delay Estimation Method Based on Canonical Correlation Analysis." JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA 32, no. 6 (2013): 548. http://dx.doi.org/10.7776/ask.2013.32.6.548.

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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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Ravichandran, S., and P. S. Ramanibai. "Plankton and related parameters of Buckingham canal at Madras, India - A canonical correlation analysis." Archiv für Hydrobiologie 114, no. 1 (1988): 117–32. http://dx.doi.org/10.1127/archiv-hydrobiol/114/1988/117.

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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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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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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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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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Dissertations / Theses on the topic "Canonical correlation analysi"

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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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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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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BERNARDINI, EMMANUELA. "On the use of shrinkage estimators in macroeconometric modeling and forecasting." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/207742.

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In the last years a growing °ow of information in the ¯eld of macroeconomy has been collected in very large databases. It is well known nevertheless that, when a large number of series is available standard statistical tools do not work well. This thesis proposes new estimators for high dimensional systems, that are an optimally weighted average of two already existing estimators, a traditional unbiased one, su®ering of a large estimation error, and a target one, having a lot of bias coming from a misspeci¯ed structural assumption, but little in terms of variance. This method is generall

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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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Castleberry, Alissa. "Integrated Analysis of Multi-Omics Data Using Sparse Canonical Correlation Analysis." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu15544898045976.

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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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Brodin, Henrik. "CCASENSE: Canonical Correlation Analysis for Estimation of Sensitivity Maps for Fast MRI." Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7953.

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<p>Magnetic Resonance Imaging is an established technology for both imaging and</p><p>functional studies in clinical and research environments. The field is still very</p><p>research intense. Two major research areas are acquisition time and signal quality.</p><p>The last decade has provided tools for more efficient possibilities of trading these</p><p>factors against each other through parallel imaging.</p><p>In this thesis one parallel imaging method, Sensitivity Encoding for fast</p><p>MRI (SENSE) is examined. An alternative solution CCASENSE is developed.</p><p>CCASENSE reduces the acquisi

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Silva, Sarah Vanessa. "A Canonical Correlation Analysis of Self-Compassion, Life Balance, and Burnout in Counselors." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7747.

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The counseling profession seeks to support and enrich the quality of life of the general public by providing effective clinical services. Many counselors struggle with practicing self-care regularly, increasing the risk of burnout. When counselors provide services while experiencing burnout, they risk harming clients being served. The conservation of resources theory suggests that there is an increased risk of maladaptive coping and burnout when there is a decrease in resources used to protect someone from experiencing stress. A quantitative survey research study using a nonprobability conveni

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Books on the topic "Canonical correlation analysi"

1

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

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Canonical analysis: A review with applications in ecology. Springer-Verlag, 1985.

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

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

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6

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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8

Gittins, Robert. Canonical Analysis: A Review with Applications in Ecology. Springer, 2011.

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Using Heliographs to Represent Canonical Correlation Analysis Results. SAGE Publications, Ltd., 2021. http://dx.doi.org/10.4135/9781529775624.

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Canonical Analysis: A Review with Applications in Ecology. Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 1985.

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Book chapters on the topic "Canonical correlation analysi"

1

Gittins, Robert. "Canonical correlations and canonical variates." In Canonical Analysis. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-69878-1_2.

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Olive, David J. "Canonical Correlation Analysis." In Robust Multivariate Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68253-2_7.

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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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Abdi, Hervé, Vincent Guillemot, Aida Eslami, and Derek Beaton. "Canonical Correlation Analysis." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110191-1.

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Afifi, A. A., and V. Clark. "Canonical correlation analysis." In Computer-Aided Multivariate Analysis. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3342-3_10.

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Conference papers on the topic "Canonical correlation analysi"

1

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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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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Seghouane, Abd-Krim, and Muhammad Ali Qadar. "Sparsity Preserved Canonical Correlation Analysis." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191350.

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Ruan, Min, Yun Li, Yun-Hao Yuan, Ji-Peng Qiang, Bin Li, and Xiao-Bo Shen. "Fuzzy fractional canonical correlation analysis." In Tenth International Conference on Digital Image Processing (ICDIP 2018), edited by Xudong Jiang and Jenq-Neng Hwang. SPIE, 2018. http://dx.doi.org/10.1117/12.2503066.

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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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J. Gonzalez, Alvaro, Li Liao, and Cathy H. Wu. "Predicting ligand binding residues using multi-positional correlations and kernel canonical correlation analysis." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2010). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706556.

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Reports on the topic "Canonical correlation analysi"

1

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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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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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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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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