Coupling Recursive Hyperspheric Classification with Linear Discriminant Analysis for Improved Results

Recursive Hyper spheric Classification (RHC) can accurately and succinctly classify large datasets by dissecting labeled vectors into their constituent groups, or hyper spheres. While RHC is a powerful classification tool, coupling RHC with other linear classifiers enhances the ability and accuracy of the classification system, improving recognition of unlabeled vectors. In this paper, RHC is paired with Linear Discriminant Analysis (LDA) for improved classification and recognition rates.