fastcca: Fast canonical correlation analysis

In decorrelate: Decorrelation Projection Scalable to High Dimensional Data

Fast canonical correlation analysis

Description

Fast Canonical correlation analysis that is scalable to high dimensional data. Uses covariance shrinkage and algorithmic speed ups to be linear time in p when p > n.

Usage

fastcca(X, Y, k = min(dim(X), dim(Y)), lambda.x = NULL, lambda.y = NULL)

Arguments

X	first matrix (n x p1)
Y	first matrix (n x p2)
k	number of canonical components to return
lambda.x	optional shrinkage parameter for estimating covariance of X. If NULL, estimate from data.
lambda.y	optional shrinkage parameter for estimating covariance of Y. If NULL, estimate from data.

Details

summary statistics of CCA

Results from standard CCA are based on the SVD of \Sigma_{xx}^{-\frac{1}{2}} \Sigma_{xy} \Sigma_{yy}^{-\frac{1}{2}}.

Uses eclairs() and empirical Bayes covariance regularization, and applies speed up of RCCA (Tuzhilina, et al. 2023) to perform CCA on n PCs and instead of p features. Memory usage is \mathcal{O}(np) instead of \mathcal{O}(p^2). Computation is \mathcal{O}(n^2p) instead of \mathcal{O}(p^3) or \mathcal{O}(np^2)

Value

fastcca object

References

Tuzhilina, E., Tozzi, L., & Hastie, T. (2023). Canonical correlation analysis in high dimensions with structured regularization. Statistical modelling, 23(3), 203-227.

decorrelate documentation built on Aug. 8, 2025, 7:55 p.m.