Log in

Relevant bibliographies by topics / Canonical correlation analysi / Journal articles

To see the other types of publications on this topic, follow the link: Canonical correlation analysi.

Journal articles on the topic 'Canonical correlation analysi'

Author: Grafiati

Published: 21 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Canonical correlation analysi.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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

Full text

Abstract:

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 selected, and their monthly stock returns were calculated using monthly stock prices. The independent variables include: consumer price index, money supply, treasury bills, exchange rate, unemployment, mining and industrial index. The CCA was used to construct the CCA model for the ZSE. Findings – Maximization of stock returns at the ZSE is mostly influenced by the changes in consumer price index, money supply, exchange rate and treasury bills. The four macroeconomic variables greatly affect the movement of stock prices which, in turn, affect stock returns. The stock returns for Hwange, Barclays, Falcon, Ariston, Border, Caps and Bindura were significant in forming the CCA model. Research limitations/implications – During the research period, some companies delisted due to economic hardships, and this reduced the sample size for stock returns for respective companies. Practical implications – The results from this research can be used by policymakers, stock market regulators and the government to make informed decisions when crafting economic policies for the country. The CCA model enables the stakeholders to identify the macroeconomic variables that play a pivotal role in maximizing the strength of the relationship with stock returns. Social implications – Macroeconomic variables, such as consumer price index, inflation, etc., directly affect the livelihoods of the general populace. They also impact on the performance of companies. The society can monitor economic trends and make the right decisions based on the current trends of economic performance. Originality/value – This research opens a new dimension to the study of macroeconomic variables and stock returns. Most studies carried out so far in Zimbabwe zeroed in on multiple regression as the central methodology. No study has been done using the CCA as the main methodology.

APA, Harvard, Vancouver, ISO, and other styles

3

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 (November 29, 1988): 117–32. http://dx.doi.org/10.1127/archiv-hydrobiol/114/1988/117.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Lipovetsky, Stan. "Canonical Concordance Correlation Analysis." Mathematics 11, no. 1 (December 26, 2022): 99. http://dx.doi.org/10.3390/math11010099.

Full text

Abstract:

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 understood as a more comprehensive characteristic of similarity, or agreement between two data sets measured simultaneously by the distance of their mean values and the distance of their variances, together with the maximum possible correlation between the aggregates of the variables in the sets. The CCCA is expressed as a generalized eigenproblem which reduces to the regular CCA if the means of the aggregates are equal, but for the different means it yields a different from CCA solution. The properties and applications of this type of multivariate analysis are described. The CCCA approach can be useful for solving various applied statistical problems when closeness of the aggregated means and variances, together with the maximum canonical correlations are needed for a general agreement between two data sets.

APA, Harvard, Vancouver, ISO, and other styles

5

Krzyśko, Mirosław, and Łukasz Waszak. "Canonical correlation analysis for functional data." Biometrical Letters 50, no. 2 (December 1, 2013): 95–105. http://dx.doi.org/10.2478/bile-2013-0020.

Full text

Abstract:

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 basis functions.

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

Abstract:

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 of multivariate analysis is useful for solving various practical problems of applied statistics requiring operating with two data sets, and can be helpful in managerial estimations and decision making.

APA, Harvard, Vancouver, ISO, and other styles

7

Zhao, Hongmin, Dongting Sun, and Zhigang Luo. "Incremental Canonical Correlation Analysis." Applied Sciences 10, no. 21 (November 4, 2020): 7827. http://dx.doi.org/10.3390/app10217827.

Full text

Abstract:

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 matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.

APA, Harvard, Vancouver, ISO, and other styles

8

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Cocozzelli, Carmelo. "Understanding Canonical Correlation Analysis." Journal of Social Service Research 13, no. 4 (August 10, 1990): 19–42. http://dx.doi.org/10.1300/j079v13n04_02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Hardoon, David R., and John Shawe-Taylor. "Sparse canonical correlation analysis." Machine Learning 83, no. 3 (November 6, 2010): 331–53. http://dx.doi.org/10.1007/s10994-010-5222-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Sakar, C. Okan, Olcay Kursun, and Fikret Gurgen. "Ensemble canonical correlation analysis." Applied Intelligence 40, no. 2 (August 13, 2013): 291–304. http://dx.doi.org/10.1007/s10489-013-0464-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Cunha, Márcio Vieira da, Mario de Andrade Lira, Mércia Virginia Ferreira dos Santos, Erinaldo Viana de Freitas, José Carlos Batista Dubeux Junior, Alexandre Carneiro Leão de Mello, and Kalina Gerciane Rodovalho Martins. "Association between the morphological and productive characteristics in the selection of elephant grass clones." Revista Brasileira de Zootecnia 40, no. 3 (March 2011): 482–88. http://dx.doi.org/10.1590/s1516-35982011000300004.

Full text

Abstract:

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 correlations were significant at 1% probability by the Chi-square test. The first pair of canonic factors, with correlation of 0.9999, related the plants with the highest dry matter content to plants with lower leaf area indexes, light perception and leaf angle. The second pair of canonic factors, with correlation of 0.9999, related the plants with the highest dry matter production to the plants with higher basal tiller density, height, and low green leaf number per tiller. The results of the path analysis indicated that the light interception is determinant in dry matter content expression of Pennisetum sp. clones, while the basal tiller density and plant height are responsible for dry matter production in these clones.

APA, Harvard, Vancouver, ISO, and other styles

13

Karmakar, Partha, Debasis Mazumdar, Seema Sarkar, and Sougata Karmakar. "Association Study Between Lead And Copper Accumulation At Different Physiological Systems Of Goat By Application Of Canonical Correlation And Canonical Correspondence Analyses." Indian Journal of Applied Research 1, no. 5 (October 1, 2011): 217–19. http://dx.doi.org/10.15373/2249555x/feb2012/79.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

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

Full text

Abstract:

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 features. We propose a model-based probabilistic approach for correlation and canonical correlation estimation for two sparse count data sets. Probabilistic sparse CCA (PSCCA) demonstrates that correlations and canonical correlations estimated at the natural parameter level are more appropriate than traditional estimation methods applied to the raw data. We demonstrate through simulation studies that PSCCA outperforms other standard correlation approaches and sparse CCA approaches in estimating the true correlations and canonical correlations at the natural parameter level. We further apply the PSCCA method to study the association of miRNA and mRNA expression data sets from a squamous cell lung cancer study, ﬁnding that PSCCA can uncover a large number of strongly correlated pairs than standard correlation and other sparse CCA approaches. Journal of Statistical Research 2022, Vol. 56, No. 1, pp. 73-98

APA, Harvard, Vancouver, ISO, and other styles

15

ter Braak, Cajo J. F. "Interpreting canonical correlation analysis through biplots of structure correlations and weights." Psychometrika 55, no. 3 (September 1990): 519–31. http://dx.doi.org/10.1007/bf02294765.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Teng, Zhongming, and Xiaowei Zhang. "A Jacobi–Davidson Method for Large Scale Canonical Correlation Analysis." Algorithms 13, no. 9 (September 12, 2020): 229. http://dx.doi.org/10.3390/a13090229.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

17

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 (December 6, 2011): 163–68. http://dx.doi.org/10.17221/4010-cjas.

Full text

Abstract:

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 < 0.01). Canonical weights and loadings from canonical correlation analysis indicated that age at sexual maturity had the largest contribution as compared with body weight and egg weight to variation of the number of egg productions at three different periods.  

APA, Harvard, Vancouver, ISO, and other styles

18

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

Full text

Abstract:

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 to encode correlations between images and their symmetry samples. To verify effectiveness, four public image databases were tested. The result was that SRCCA was more robust than CCA and had good performance for image classification.

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.

Full text

Abstract:

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 probabilistic CCA. The model is then generalized to an arbitrary number of views. An empirical analysis confirms that the proposed deep multi-view model can discover subtle relationships between multiple views and recover rich representations.

APA, Harvard, Vancouver, ISO, and other styles

21

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Chen, Jia, Gang Wang, and Georgios B. Giannakis. "Graph Multiview Canonical Correlation Analysis." IEEE Transactions on Signal Processing 67, no. 11 (June 1, 2019): 2826–38. http://dx.doi.org/10.1109/tsp.2019.2910475.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

MIN, Wenwen, Juan LIU, and Shihua ZHANG. "Sparse Weighted Canonical Correlation Analysis." Chinese Journal of Electronics 27, no. 3 (May 1, 2018): 459–66. http://dx.doi.org/10.1049/cje.2017.08.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Tenenhaus, Arthur, and Michel Tenenhaus. "Regularized Generalized Canonical Correlation Analysis." Psychometrika 76, no. 2 (March 17, 2011): 257–84. http://dx.doi.org/10.1007/s11336-011-9206-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

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

Full text

Abstract:

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 substantial correlation with it, whereby correlations of tests of serial processing were systematically higher than correlation of tests of parallel and particularly of perceptual processing. Canonic factor derived from tests of aggressiveness was not similar to invert scaled factor of aggressiveness of second order because tests of basic aggressiveness and impulsiveness that were otherwise dominant salient of general factor of aggressiveness, did not have significant correlation with canonical factor derived from tests of aggressiveness. According to that, it seams that inferior functioning of cognitive and particularly serial processor is connected only to those modalities of aggressiveness where there are also disorders of other conative regulators, especially systems for coordination and control of conative functions, but not with basic aggressiveness. That, basically biological characteristic of system for regulation and control of attack reaction, is not, according to all, in significant correlation with efficiency of cognitive processors.

APA, Harvard, Vancouver, ISO, and other styles

32

V., Krishnakumar. "Parallel Processing Framework for Minority Oversampling in Kernel Canonical Correlation Analysis (KCCA) Adaptive Subspaces for Class Imbalanced Datasets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 250–61. http://dx.doi.org/10.5373/jardcs/v12sp4/20201487.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Seok, Jong-Won, Tae-Hwan Kim, and Keun-Sung Bae. "Underwater Target Analysis Using Canonical Correlation Analysis." Journal of the Korean Institute of Information and Communication Engineering 16, no. 9 (September 30, 2012): 1878–83. http://dx.doi.org/10.6109/jkiice.2012.16.9.1878.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

PENG, Yan, and Dao-Qiang ZHANG. "Semi-Supervised Canonical Correlation Analysis Algorithm." Journal of Software 19, no. 11 (April 7, 2009): 2822–32. http://dx.doi.org/10.3724/sp.j.1001.2008.02822.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Pimentel, Harold, Zhiyue Hu, and Haiyan Huang. "Biclustering by sparse canonical correlation analysis." Quantitative Biology 6, no. 1 (February 9, 2018): 56–67. http://dx.doi.org/10.1007/s40484-017-0127-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

LAI, P. L., and C. FYFE. "KERNEL AND NONLINEAR CANONICAL CORRELATION ANALYSIS." International Journal of Neural Systems 10, no. 05 (October 2000): 365–77. http://dx.doi.org/10.1142/s012906570000034x.

Full text

Abstract:

We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.

APA, Harvard, Vancouver, ISO, and other styles

45

Fyfe, Colin, Gayle Leen, and Pei Ling Lai. "Gaussian processes for canonical correlation analysis." Neurocomputing 71, no. 16-18 (October 2008): 3077–88. http://dx.doi.org/10.1016/j.neucom.2008.04.037.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Marzban, Caren, Scott Sandgathe, and James D. Doyle. "Model Tuning with Canonical Correlation Analysis." Monthly Weather Review 142, no. 5 (April 30, 2014): 2018–27. http://dx.doi.org/10.1175/mwr-d-13-00245.1.

Full text

Abstract:

Abstract Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a well-established multivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. The method is applied to several model parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) for the purpose of “controlling” three forecast quantities: 1) convective precipitation, 2) stable precipitation, and 3) snow. It is shown that the model parameters employed here can be set to affect the sum, and the difference between convective and stable precipitation, while keeping snow mostly constant; a different combination of model parameters is shown to mostly affect the difference between stable precipitation and snow, with minimal effect on convective precipitation. In short, the proposed method cannot only capture the complex relationship between model parameters and forecast quantities, it can also be utilized to optimally control certain combinations of the latter.

APA, Harvard, Vancouver, ISO, and other styles

47

Lee, Keunbaik, and Jae Keun Yoo. "Canonical Correlation Analysis Through Linear Modeling." Australian & New Zealand Journal of Statistics 56, no. 1 (January 21, 2014): 59–72. http://dx.doi.org/10.1111/anzs.12057.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Wang, Li, Lei-hong Zhang, Zhaojun Bai, and Ren-Cang Li. "Orthogonal canonical correlation analysis and applications." Optimization Methods and Software 35, no. 4 (January 20, 2020): 787–807. http://dx.doi.org/10.1080/10556788.2019.1700257.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Graffelman, Jan. "Enriched biplots for canonical correlation analysis." Journal of Applied Statistics 32, no. 2 (March 2005): 173–88. http://dx.doi.org/10.1080/02664760500054202.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Haixian Wang. "Local Two-Dimensional Canonical Correlation Analysis." IEEE Signal Processing Letters 17, no. 11 (November 2010): 921–24. http://dx.doi.org/10.1109/lsp.2010.2071863.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!