Alternating Least Squares Algorithms for Simultaneous Components Analysis with Equal Component Weight Matrices in Two or More Populations

1 September 1989

journal article
research article
Published by Cambridge University Press (CUP) in Psychometrika

Vol. 54 (3) , 467-473
https://doi.org/10.1007/bf02294629

Abstract

Millsap and Meredith (1988) have developed a generalization of principal components analysis for the simultaneous analysis of a number of variables observed in several populations or on several occasions. The algorithm they provide has some disadvantages. The present paper offers two alternating least squares algorithms for their method, suitable for small and large data sets, respectively. Lower and upper bounds are given for the loss function to be minimized in the Millsap and Meredith method. These can serve to indicate whether or not a global optimum for the simultaneous components analysis problem has been attained.

Keywords

This publication has 3 references indexed in Scilit:

Component Analysis in Cross-Sectional and Longitudinal Data
Psychometrika, 1988
Rotation to Perfect Congruence and the Cross Validation of Component Weights Across Populations
Multivariate Behavioral Research, 1986
On Component Analyses
Psychometrika, 1985