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A Tutorial on Canonical Correlation Methods

Uurtio, V; Monteiro, JM; Kandola, J; Shawe-Taylor, J; Fernandez-Reyes, D; Rousu, J; (2018) A Tutorial on Canonical Correlation Methods. ACM Computing Surveys (CSUR) , 50 (6) , Article 95. 10.1145/3136624. Green open access

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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.

Type: Article
Title: A Tutorial on Canonical Correlation Methods
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3136624
Publisher version: http://dx.doi.org/10.1145/3136624
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Canonical correlation, Regularisation, Kernel methods, Sparsity
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10043022
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