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In this paper, we formulate a new generalized reference kernel hoping toimprove the original base kernel using a set of reference vectors. Depending onthe selected reference vectors, our formulation shows similarities toapproximate kernels, random mappings, and Non-linear Projection Trick. Focusingon small-scale one-class classification, our analysis and experimental resultsshow that the new formulation provides approaches to regularize, adjust therank, and incorporate additional information into the kernel itself, leading toimproved one-class classification accuracy.

Generalized Reference Kernel for One-class Classification