Dimensionality reduction by using transductive learning and binary hierarchical trees

Abstract

In this study, transductive learning and binary hierarchical decision trees are used together to find discriminative embedding (projection) directions. The projection directions returned by the proposed methodology are used for dimensionality reduction and the accuracy of nearest neighbor classification is significantly improved. We choose random classes… (More)

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