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Learning Canonical Correlations
 In Proceedings of the 10th Scandinavian Conference on Image Analysis
, 1997
"... This paper presents a novel learning algorithm that nds the linear combination of one set of multidimensional variates that is the best predictor, and at the same time nds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation and ha ..."
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Cited by 18 (10 self)
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This paper presents a novel learning algorithm that nds the linear combination of one set of multidimensional variates that is the best predictor, and at the same time nds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation
Diagnostics for Canonical Correlation
"... Canonical correlation analysis is a versatile multivarite technique that is prone to distortion as a result of correlation outliers. The detection and treatment of such outliers is complicated by outlier masking effects. Methods that check the effect of one observation at a time are therefore unsucc ..."
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Canonical correlation analysis is a versatile multivarite technique that is prone to distortion as a result of correlation outliers. The detection and treatment of such outliers is complicated by outlier masking effects. Methods that check the effect of one observation at a time are therefore
Restricted Canonical Correlations
, 1993
"... Given a pdimensional random variable yell and a qdimensional random variabley(2), the first canonical correlation leads to finding Q * E Rp and (3 * E R q which maximizes the correlation between Q'y(l) and (3'y(2). However, in many practical situations (e.g. educational testing problems ..."
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Cited by 9 (0 self)
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Given a pdimensional random variable yell and a qdimensional random variabley(2), the first canonical correlation leads to finding Q * E Rp and (3 * E R q which maximizes the correlation between Q'y(l) and (3'y(2). However, in many practical situations (e.g. educational testing
Deep Canonical Correlation Analysis
"... We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correla ..."
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Cited by 20 (4 self)
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We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total
FUNDAMENTALS OF CANONICAL CORRELATION ANALYSIS:
"... Canonical correlation analysis is illustrated and three common fallacious interpretation practices are described. Simply, canonical correlation is an example of the bivariate case. Like all parametric methods, it involves the creation of synthetic scores for each person. It presumes at least two pre ..."
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Canonical correlation analysis is illustrated and three common fallacious interpretation practices are described. Simply, canonical correlation is an example of the bivariate case. Like all parametric methods, it involves the creation of synthetic scores for each person. It presumes at least two
Canonical Correlation Analysis Description
, 2008
"... Description This package provides an alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods. A method for generating helio plots is also included. License GPL (>=3) LazyLoad yes R topics documented: cca..................................... ..."
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Description This package provides an alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods. A method for generating helio plots is also included. License GPL (>=3) LazyLoad yes R topics documented: cca
On the regularization of canonical correlation analysis
 in Proceedings of the International Conference on Independent Component Analysis and Blind Source Separation (ICA2003), S
, 2003
"... By elucidating a parallel between canonical correlation analysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and interpreted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have an im ..."
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Cited by 8 (1 self)
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By elucidating a parallel between canonical correlation analysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and interpreted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have
Sparse canonical correlation analysis
 Machine Learning Journal
, 2011
"... 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 ..."
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Cited by 22 (0 self)
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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
Nonlinear Canonical Correlation Analysis of the . . .
 J. CLIMATE
, 2001
"... Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear canonical correlation analysis (NLCCA) method is used to study the relation ..."
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Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear canonical correlation analysis (NLCCA) method is used to study
Results 1  10
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1,988