A note on the optimality of the Karhunen-Loeve expansion
Pattern Recognition Letters
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Vectorcardiogram (VCG) data often are analyzed using the Karhunen-Loeve expansion of the sample covariance matrix, S, as a method for discriminating between the VCG's of healthy pat unhealthy patients. The estimator, S, however, can be seriously effected by both atypical observations and the number of VCG's in the database relative to their dimension. In this paper it is shown that alternative robust estimators of the covariance matrix are appealing in analyzing VCG data when outliers are present in the sample. Also, it is demonstrated that sample sizes in such experiments should be greatly expanded in order to validate the asymptotic properties of S.