LINEAR METHODS IN MULTIMODE DATA-ANALYSIS FOR DECISION-MAKING

This paper presents and analyses several methods for the evaluation of information given in the form of many-way matrices. These methods are based on the least squares approximation of a matrix by a many-vector product which can be represented as a nonlinear eigenvector problem. Using real data about university choice by high school graduates in Is rael, we develop and compare the following three families of methods: parallel proportional profiles, various types of methods based on the use of cyclic matrices (canonical correlations, principal components, and planes' approximation), and minimization of relative deviations.