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Canonical Correlations and Nonlinear Dependencies [PDF]
Symmetry, 2021 Canonical correlation analysis (CCA) is the default method for investigating the linear dependence structure between two random vectors, but it might not detect nonlinear dependencies. This paper models the nonlinear dependencies between two random vectors by the perturbed independence distribution, a multivariate semiparametric model where CCA ...
exaly +4 more sources
A Multifunctional Sensor in Ternary Solution Using Canonical Correlations for Variable Links Assessment [PDF]
Sensors, 2016 Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well.
Dan Liu +5 more
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Integrative correlation: Properties and relation to canonical correlations. [PDF]
J Multivar Anal, 2014 The integrative correlation coefficient was developed to facilitate the validation of expression microarray results in public datasets, by identifying genes that are reproducibly measured across studies and even across microarray platforms. In the current study, we develop a number of interesting and important mathematical and statistical properties of
Cope L, Naiman DQ, Parmigiani G.
europepmc +5 more sources
Aksioma: Jurnal Program Studi Pendidikan Matematika, 2023 Although previous research has demonstrated that school climate (leading to a climate of competition or cooperation) is related to student achievement, including their literacy skills, there is still insufficient evidence which of the two school climates
Rasmuin Rasmuin
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Canonical Concordance Correlation Analysis
Mathematics, 2022 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 ...
Stan Lipovetsky
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Hydropedological behavior of a chromic vertisol under different plant covers
Revista de Ciencias Agrícolas, 2021 Vertisols present edaphic limiting factors primarily of a physical nature. The study of their hydropedology, supported by multivariate techniques, is of vital importance.
Juan Alejandro Villazón-Gómez
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Longitudinal canonical correlation analysis
Journal of the Royal Statistical Society Series C: Applied Statistics, 2023 AbstractThis paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modelled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space.
Seonjoo Lee +3 more
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A Tutorial on Canonical Correlation Methods [PDF]
ACM Computing Surveys, 2017 Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the ...
Monteiro, João M +6 more
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Multiset Canonical Correlations Analysis With Global Structure Preservation
IEEE Access, 2020 This paper considers unsupervised dimensionality reduction of multi-view data, where locality preserving canonical correlation analysis and a new locality-preserving canonical correlation analysis are two typical effective methods.
Hongjie Zhang +3 more
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Sparse canonical correlation analysis [PDF]
Machine Learning, 2010 We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view.
David R. Hardoon, John Shawe-Taylor
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