A Tutorial on Canonical Correlation Methods

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University College London
  • University College of London
  • Imperial College London

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.

Details

Original languageEnglish
Article number95
Pages (from-to)1-33
JournalACM COMPUTING SURVEYS
Volume50
Issue number6
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • canonical correlation, regularisation, kernel methods, sparsity

ID: 16812888