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Most of the supervised dimensionality reduction (DR) methods design interclass scatter as the separability between the class means, which may force to assume unimodal Gaussian likelihoods and their projection space trends toward the class means. This paper presents a novel DR approach, norm discriminant eigenspace transform (NDET), in which average norms (l₂) of classes have been utilized to characterize the interclass separability and the within-class distance characterizes the intraclass compactness. ND...