A Tutorial on Canonical Correlation Methods

TL;DR

This tutorial provides a comprehensive overview of canonical correlation analysis (CCA), including its variants like regularised, kernel, sparse, deep, and Bayesian CCA, with practical guidance and examples for data analysis.

Contribution

It offers a unified, accessible overview of CCA methods, covering theory, extensions, optimization, significance testing, and interpretation, serving as a practical guide.

Findings

Explains the theory and variants of CCA including regularised, kernel, sparse, deep, and Bayesian methods.

Provides numerical examples demonstrating the application of different CCA techniques.

Serves as a comprehensive, practical tutorial for applying CCA in data analysis.

Abstract

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 relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.