tutorial

A Tutorial on Canonical Correlation Methods

Authors:

João M. Monteiro,

John Shawe-Taylor,

Delmiro Fernandez-Reyes,

Juho RousuAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 6

Article No.: 95, Pages 1 - 33

https://doi.org/10.1145/3136624

Published: 22 November 2017 Publication History

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.

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Index Terms

A Tutorial on Canonical Correlation Methods
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Dimensionality reduction and manifold learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 6

November 2018

752 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3161158

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Published: 22 November 2017

Accepted: 01 August 2017

Revised: 01 July 2017

Received: 01 February 2017

Published in CSUR Volume 50, Issue 6

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