Robust canonical discriminant analysis |
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Authors: | Peter Verboon Ivo A. van der Lans |
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Affiliation: | (1) Department of Data Theory, University of Leiden, The Netherlands;(2) 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.The research of the first author was supported by the Netherlands Organization of Scientific Research (NWO grant 560-267-029). |
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Keywords: | canonical discriminant analysis robustness outliers optimal scaling majorization |
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