A Nonmetric Approach to Linear Discriminant Analysis

Abstract A new nonmetric linear discriminant analysis approach is proposed that is based on the maximization of an index of separation differing from that used by the classical method. The possibility of choosing between Fisher's classical discriminant function and the one proposed here enables us to reduce the number of misclassifications for given data. The method is exemplified on empirical data and various simulations and is compared with the classical linear discriminant analysis.

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