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Robust Canonical Discriminant Analysis

Published online by Cambridge University Press:  01 January 2025

Peter Verboon and
Ivo A. van der Lans
Peter Verboon*
Affiliation:
Department of Statistical Methods, Statistics Netherlands
Ivo A. van der Lans
Affiliation:
Department of Data Theory, University of Leiden
*
Requests for reprints should be sent to Peter Verboon, Department of Statistical Methods, Statistics Netherlands, P.O. Box 959, 2270 AZ Voorburg, THE NETHERLANDS.
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Abstract

A method for robust canonical discriminant analysis via two robust objective loss functions is discussed. These functions are useful to reduce the influence of outliers in the data. Majorization is used at several stages of the minimization procedure to obtain a monotonically convergent algorithm. An advantage of the proposed method is that it allows for optimal scaling of the variables. In a simulation study it is shown that under the presence of outliers the robust functions outperform the ordinary least squares function, both when the underlying structure is linear in the variables as when it is nonlinear. Furthermore, the method is illustrated with empirical data.

Keywords

canonical discriminant analysisrobustnessoutliersoptimal scalingmajorization

Information

Type
Original Paper
Information
Psychometrika , Volume 59 , Issue 4 , December 1994, pp. 485 - 507
Copyright
Copyright © 1994 The Psychometric Society

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Footnotes

The research of the first author was supported by the Netherlands Organization of Scientific Research (NWO grant 560-267-029).

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