Generalized canonical correlation analysis with missing values

Abstract

Two new methods for dealing with missing values in generalized canonicalcorrelation analysis are introduced. The first approach, which does notrequire iterations, is a generalization of the Test Equating method availablefor principal component analysis. In the second approach, missing values areimputed in such a way that the generalized canonical correlation analysisobjective function does not increase in subsequent steps. Convergence isachieved when the value of the objective function remains constant. By meansof a simulation study, we assess the performance of the new methods. Wecompare the results with those of two available methods; the missing-datapassive method, introduced Gifi's homogeneity analysis framework, and theGENCOM algorithm developed by Green and Carroll.generalized canoncial correlation analysis;missing values