Instead, the canonical correlation is the preferred measure of effect size.

This property is the basis for principal components analysis and canonical correlation.

Canonical correlation analysis of time series and the use of an information criterion.

The canonical correlations is equal to the cosine of principal angles.

He also introduced canonical correlation analysis.

In statistics, canonical correlation analysis is a way of making sense of cross-covariance matrices.

Canonical factor analysis seeks factors which have the highest canonical correlation with the observed variables.

(1941) The statistical significance of canonical correlation.

Understanding canonical correlation analysis (Concepts and Techniques in Modern Geography)

We notice that in both cases , which illustrates that the canonical correlation analysis treats correlated and anticorrelated variables similarly.

Canonical correlation analysis seeks vectors and such that the random variables and maximize the correlation .

PCA is also related to canonical correlation analysis (CCA).

Canonical Correlation Analysis is used combining 31 leaf characters, but leaf margin type represented a significant component of the relationship between physiognomic states and temperature.

A relatively simple application of the information bottleneck is to Gaussian variates and this has some semblance to a least squares reduced rank or canonical correlation.

A typical use for canonical correlation in the experimental context is to take two sets of variables and see what is common amongst the two sets.

Fortunately, the generalized canonical correlation analysis (gCCA) is the statistical method of choice for making those harmful cross-covariances vanish.

Canonical correlation / (F:R)

Canonical correlation analysis finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate correlation.

If it is band-diagonal it can be transformed into a block-diagonal form e.g. by means of a generalised Canonical Correlation Analysis (gCCA).

TTP-based: Tensor Canonical Correlation Analysis (TCCA)

TVP-based: Multilinear Canonical Correlation Analysis (MCCA)

Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical correlation analysis.

In statistics, the generalized canonical correlation analysis (gCCA), is a way of making sense of cross-correlation matrices between the sets of random variables when there are more than two sets.

Visualization of the results of canonical correlation is usually through bar plots of the coefficients of the two sets of variables for the pairs of canonical variates showing significant correlation.

A note on the ordinal canonical correlation analysis of two sets of ranking scores (Also provides a FORTRAN program)- in J. of Quantitative Economics 7(2), 2009, pp.