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Journal articles on the topic 'Correlation analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Bolshakova, Lyudmila Valentinovna. "Correlation and Regression Analysis of Economic Problems." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 2077–88. http://dx.doi.org/10.47059/revistageintec.v11i3.2074.

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2

NAOKO, Fujii. "LECTURE: Correlation Analysis." Journal of exercise physiology 6, no. 3 (1991): 127–32. http://dx.doi.org/10.1589/rika1986.6.127.

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3

Strack, Rita. "Comprehensive correlation analysis." Nature Methods 16, no. 1 (December 20, 2018): 25. http://dx.doi.org/10.1038/s41592-018-0279-5.

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4

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.

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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 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.

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5

Stephenson, Peter. "Analysis and Correlation." Computer Fraud & Security 2002, no. 12 (December 2002): 16–18. http://dx.doi.org/10.1016/s1361-3723(02)01214-9.

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6

Chen, Xiaohong, Songcan Chen, and Hui Xue. "Large correlation analysis." Applied Mathematics and Computation 217, no. 22 (July 2011): 9041–52. http://dx.doi.org/10.1016/j.amc.2011.03.117.

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7

Yoshioka, Tomohiko, Yasuyuki Morita, Mitsugu Todo, Yasuyuki Matsusita, and Kazuo Arakawa. "P-19 Deformation Analysis of Periodontal Tissue using Digital Image Correlation Analysis." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2007.3 (2007): S107. http://dx.doi.org/10.1299/jsmeapbio.2007.3.s107.

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8

Faltermeier, Rupert, Martin A. Proescholdt, Sylvia Bele, and Alexander Brawanski. "Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology." Computational and Mathematical Methods in Medicine 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/652030.

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Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

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9

Holmes, R. B. "On Random Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 12, no. 2 (April 1991): 239–72. http://dx.doi.org/10.1137/0612019.

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10

Lipovetsky, Stan. "Canonical Concordance Correlation Analysis." Mathematics 11, no. 1 (December 26, 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 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.

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11

Dashora, Sanchita, Medhavi Sharma, and Rajrani Sharma. "Analysis of Hysterectomies and Clinicopathological Correlation: A Prospective Study." Indian Journal of Obstetrics and Gynecology 6, no. 1 (2018): 7–14. http://dx.doi.org/10.21088/ijog.2321.1636.6118.1.

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12

Tawfiq, Sherwan I., Dana A. Abdulkhaleq, and Shara J. Hama. "Correlation and Path Analysis in Barley under Rainfall Conditions." Journal of Zankoy Sulaimani - Part A 18, no. 3 (June 5, 2016): 99–106. http://dx.doi.org/10.17656/jzs.10538.

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13

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.

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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 basis functions.

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14

Teplova (Kosolapova), N., K. Itoh, S.-I. Itoh, E. Gusakov, S. Heuraux, S. Inagaki, M. Sasaki, et al. "On turbulence-correlation analysis based on correlation reflectometry." Physica Scripta 87, no. 4 (February 28, 2013): 045502. http://dx.doi.org/10.1088/0031-8949/87/04/045502.

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15

Aizawa, Naoyuki, and Emiko Nakasone. "Statistical Method. Correlation Analysis." Journal of exercise physiology 4, no. 4 (1989): 223–29. http://dx.doi.org/10.1589/rika1986.4.223.

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16

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.

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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 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.

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17

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.

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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 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.

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18

Novak, A. I., and Y. O. Lyashchuk. "Biological risk correlation analysis." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 4 (February 11, 2020): 40–45. http://dx.doi.org/10.20914/2310-1202-2019-4-40-45.

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19

NAWA, Kotaro. "Patents of correlation analysis." Journal of Information Processing and Management 51, no. 10 (2009): 785–86. http://dx.doi.org/10.1241/johokanri.51.785.

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20

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.

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21

Kotiah, Thoddi C. T. "Correlation analysis in calculus." International Journal of Mathematical Education in Science and Technology 23, no. 5 (September 1992): 671–82. http://dx.doi.org/10.1080/0020739920230505.

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22

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.

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23

Larner, Ken, and Valmore Celis. "Selective-correlation velocity analysis." GEOPHYSICS 72, no. 2 (March 2007): U11—U19. http://dx.doi.org/10.1190/1.2435702.

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Increased resolution in computed velocity spectra aids in distinguishing between neighboring primary events from reflectors with conflicting dip and in identifying primaries in the presence of multiples. The transformation from the offset and reflection-time domain to the stacking-velocity and zero-offset-time domain can be achieved using any of several coherence measures based on crosscorrelations among traces in a common-midpoint (CMP) gather or a common-image gather (CIG). Use of just selected subsets of crosscorrelations rather than all possible ones in a gather can improve both the reliability and resolution of velocity analysis. In selective-correlation velocity analysis, we include in the summation only crosscorrelations for those pairs of traces with relative differential moveout of reflections exceeding a chosen threshold value. Comparisons of performance on CMP gathers, both synthetic and field-data, show that selective-correlation velocity analysis considerably enhances the resolving power of velocity spectra over that of conventional crosscorrelation sum (normalized or unnormalized) in the presence of closely interfering reflections, statics distortions, and random noise, at no sacrifice in quality of results, and does so at computational cost comparable to that for conventional velocity analysis.

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24

Kraftmakher, Yaakov. "Correlation analysis with ScienceWorkshop." American Journal of Physics 70, no. 7 (July 2002): 694–97. http://dx.doi.org/10.1119/1.1475330.

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25

Harrington, Peter de B., Aaron Urbas, and Peter J. Tandler. "Two-dimensional correlation analysis." Chemometrics and Intelligent Laboratory Systems 50, no. 2 (March 2000): 149–74. http://dx.doi.org/10.1016/s0169-7439(99)00062-3.

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26

Shi, Runhua, and Steven A. Conrad. "Correlation and regression analysis." Annals of Allergy, Asthma & Immunology 103, no. 4 (October 2009): S35—S41. http://dx.doi.org/10.1016/s1081-1206(10)60820-4.

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27

Zhang, Enhao, Xiaohong Chen, and Liping Wang. "Consistent Discriminant Correlation Analysis." Neural Processing Letters 52, no. 1 (June 27, 2020): 891–904. http://dx.doi.org/10.1007/s11063-020-10285-w.

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28

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.

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29

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.

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30

Yang, Qingxi, Qiaokun Kang, Qingyang Huang, Zenghui Cui, Yu Bai, and Huanan Wei. "Linear correlation analysis of ammunition storage environment based on Pearson correlation analysis." Journal of Physics: Conference Series 1948, no. 1 (June 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1948/1/012064.

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31

Grone, Robert, and Stephen Pierce. "Permanental Inequalities for Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 9, no. 2 (April 1988): 194–201. http://dx.doi.org/10.1137/0609016.

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32

Costa, Natália, César Silva, and Paulo Ferreira. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies." International Journal of Financial Studies 7, no. 3 (September 15, 2019): 51. http://dx.doi.org/10.3390/ijfs7030051.

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In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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33

Prevost, A. Toby, Dan Mason, Simon Griffin, Ann-Louise Kinmonth, Stephen Sutton, and David Spiegelhalter. "Allowing for correlations between correlations in random-effects meta-analysis of correlation matrices." Psychological Methods 12, no. 4 (December 2007): 434–50. http://dx.doi.org/10.1037/1082-989x.12.4.434.

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34

Joe, Harry. "Generating random correlation matrices based on partial correlations." Journal of Multivariate Analysis 97, no. 10 (November 2006): 2177–89. http://dx.doi.org/10.1016/j.jmva.2005.05.010.

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35

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.

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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 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

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36

Balakrishna, P., Rajasekhar Pinnamaneni, KV Pavani, and RK Mathur. "Correlation and Path Coefficient Analysis in Indian Oil Palm genotypes." Journal of Pure and Applied Microbiology 12, no. 1 (March 30, 2018): 195–206. http://dx.doi.org/10.22207/jpam.12.1.25.

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37

Dr.K, Uma Pavan Kumar, and Kalimuthu Dr.M. "Performance Analysis of Naïve Bayes Correlation Models in Machine Learning." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 29, 2020): 1153–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr201088.

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38

Wei, Xiaowei, Hongbo Zhang, Xinghui Gong, Xingchen Wei, Chiheng Dang, and Tong Zhi. "Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China." International Journal of Environmental Research and Public Health 17, no. 7 (April 2, 2020): 2410. http://dx.doi.org/10.3390/ijerph17072410.

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The purpose of this study is to illustrate intrinsic correlations and their temporal evolution between hydro-meteorological elements by building three-element-composed system, including precipitation (P), runoff (R), air temperature (T), evaporation (pan evaporation, E), and sunshine duration (SD) in the Wuding River Basin (WRB) in Loess Plateau, China, and to provide regional experience to correlational research of global hydro-meteorological data. In analysis, detrended partial cross-correlation analysis (DPCCA) and temporal evolution of detrended partial-cross-correlation analysis (TDPCCA) were employed to demonstrate the intrinsic correlation, and detrended cross-correlation analysis (DCCA) coefficient was used as comparative method to serve for performance tests of DPCCA. In addition, a novel way was proposed to estimate the contribution of a variable to the change of correlation between other two variables, namely impact assessment of correlation change (IACC). The analysis results in the WRB indicated that (1) DPCCA can analyze the intrinsic correlations between two hydro-meteorological elements by removing potential influences of the relevant third one in a complex system, providing insights on interaction mechanisms among elements under changing environment; (2) the interaction among P, R, and E was most strong in all three-element-composed systems. In elements, there was an intrinsic and stable correlation between P and R, as well as E and T, not depending on time scales, while there were significant correlations on local time scales between other elements, i.e., P-E, R-E, P-T, P-SD, and E-SD, showing the correlation changed with time-scales; (3) TDPCCA drew and highlighted the intrinsic correlations at different time-scales and its dynamics characteristic between any two elements in the P-R-E system. The results of TDPCCA in the P-R-E system also demonstrate the nonstationary correlation and may give some experience for improving the data quality. When establishing a hydrological model, it is suitable to only use P, R, and E time series with significant intrinsic correlation for calibrating model. The IACC results showed that taking pan evaporation as the representation of climate change (barring P), the impacts of climate change on the non-stationary correlation of P and R was estimated quantitatively, illustrating the contribution of climate to the correlation variation was 30.9%, and that of underlying surface and direct human impact accounted for 69.1%.

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39

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.

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40

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.

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41

Chhetry, Devendra, Jan De Leeuw, and Allan R. Sampson. "Monotone Correlation and Monotone Disjunct Pieces." SIAM Journal on Matrix Analysis and Applications 11, no. 3 (July 1990): 361–68. http://dx.doi.org/10.1137/0611024.

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42

Li, Chi-Kwong, and Bit-Shun Tam. "A Note on Extreme Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 15, no. 3 (July 1994): 903–8. http://dx.doi.org/10.1137/s0895479892240683.

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43

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.

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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 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.

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44

YIN, YI, and PENGJIAN SHANG. "MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS." Fractals 22, no. 04 (November 12, 2014): 1450007. http://dx.doi.org/10.1142/s0218348x14500078.

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In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.

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45

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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46

Kilic, Selim. "Interpretation of correlation analysis results." Journal of Mood Disorders 2, no. 4 (2012): 191. http://dx.doi.org/10.5455/jmood.20121209012824.

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47

Il'yasov, R. Kh. "Spline analysis of thread correlation." Economic Analysis: Theory and Practice 19, no. 1 (January 30, 2020): 173–87. http://dx.doi.org/10.24891/ea.19.1.173.

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48

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.

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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 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.

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49

Zhu, Yanmin, Shuzhi Su, Gaoming Yang, Bin Ge, and Ping Zheng. "TWO-VIEW MEDIAN CORRELATION ANALYSIS." International Journal of Applied Mathematics and Machine Learning 9, no. 1 (September 10, 2019): 27–34. http://dx.doi.org/10.18642/ijamml_7100122011.

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50

Wysocki, W. "Maximal correlation in path analysis." Applicationes Mathematicae 21, no. 2 (1991): 225–33. http://dx.doi.org/10.4064/am-21-2-225-233.

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