Generalized Canonical Correlation Analysis of Matrices with Different Row and Column Orders

Abstract

A Method is offered that makes it possible to apply generalized canonical correlations analysis (CANCOR) to two or more matrices of different row and column order. The new method optimizes the generalized canonical correlation analysis objective by considering only the observed values. This is achieved by employing selection matrices. We present and discuss fit measures to assess the quality of the solutions. In a simulation study we assess the performance of our new method and compare it to an existing procedure called GENCOM, proposed by Green and Carroll. We find that our new method outperforms the GENCOM algorithm both with respect to model fit and recovery of the true structure. Moreover, as our new method does not require any type of iteration it is easier to implement and requires less computation. We illustrate the method by means of an example concerning the relative positions of the political parties in the Netherlands based on provincial data.

Keywords: Generalized canonical correlation analysis, perceptual mapping

JEL Classification: C19, C88, M31