On the approximation of the between-set correlation matrix by canonical correlation analysis

TL;DR

This paper improves the approximation of the between-set correlation matrix in canonical correlation analysis by proposing an adjustment method and an efficient algorithm, enhancing visualization and interpretability.

Contribution

It introduces an adjustment technique for the correlation matrix using scalar and effect-based modifications, along with an alternating least squares algorithm for optimal low-rank approximation.

Findings

Adjusted analysis achieves lower root mean squared error compared to classic CCA.

Enhanced biplot visualization with minimal interpretative changes.

Software implementation available in R for practical application.

Abstract

Canonical correlation analysis is a classic well-known multivariate statistical method focusing on the relationships between two sets of variables. The visualisation of those relationships can be achieved by means of a biplot of the between-set correlation matrix. The canonical analysis provides a low-rank approximation to the between-set correlation matrix that is optimal in a generalised least squares sense. This article proposes to adjust the between-set correlation matrix using either a single scalar effect, or column and/or row effects. An alternating generalised least squares algorithm is proposed to obtain optimal adjustments and low-rank factorisations. The adjustment leads to a better approximation of the between-set correlation matrix that achieves a lower root mean squared error in comparison with the classic canonical analysis. The results of the adjusted analysis can be efficiently visualised using biplots, with a minimal change in interpretation rules that only affects the biplot origin. Biplot calibration is used to enhance the visualisation of the results of the adjusted analysis. Some examples with publicly available data sets from social science, geochemistry and medical science illustrate the proposed improvement. Software for carrying out the adjusted canonical analysis in the R environment is provided.