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On the spectral decomposition in normal discriminant analysis. (English) Zbl 1333.62056

Summary: This article enlarges the covariance configurations, on which the classical linear discriminant analysis is based, by considering the four models arising from the spectral decomposition when eigenvalues and/or eigenvectors matrices are allowed to vary or not between groups. As in the classical approach, the assessment of these configurations is accomplished via a test on the training set. The discrimination rule is then built upon the configuration provided by the test, considering or not the unlabeled data. Numerical experiments, on simulated and real data, have been performed to evaluate the gain of our proposal with respect to the linear discriminant analysis.

MSC:

62F03 Parametric hypothesis testing
62F07 Statistical ranking and selection procedures
62F30 Parametric inference under constraints
62H30 Classification and discrimination; cluster analysis (statistical aspects)

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References:

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