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The Minimal Transformation to Orthonormality

Published online by Cambridge University Press:  01 January 2025

Richard M. Johnson*
Affiliation:
The Procter & Gamble Company

Abstract

The solution is presented to the problem of finding that orthonormal matrix closest in the least-square sense to a given matrix of full rank. An application is shown to the problem in multiple regression analysis of determining the “importance” of each independent variable.

Type
Original Paper
Copyright
Copyright © 1966 Psychometric Society

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References

Eckart, C. and Young, G. The approximation of one matrix by another of lower rank. Psychometrika, 1936, 1, 211218.CrossRefGoogle Scholar
Gibson, W. A. On the least-squares orthogonalization of an oblique transformation. Psychometrika, 1962, 27, 193196.CrossRefGoogle Scholar
Gibson, W. A. Orthogonal predictors: a possible resolution of the Hoffman-Ward controversy. Psychol. Rep., 1962, 11, 3234.CrossRefGoogle Scholar
Horst, P. Matrix algebra for social scientists, New York: Holt, Rinehart and Winston, 1963.Google Scholar
Johnson, R. M. On a theorem stated by Eckart and Young. Psychometrika, 1963, 28, 259263.CrossRefGoogle Scholar