Generalized Reference Kernel for One-class Classification

TL;DR

This paper introduces a generalized reference kernel for one-class classification that leverages reference vectors to enhance kernel performance, offering better regularization, rank adjustment, and information integration.

Contribution

It proposes a novel generalized reference kernel that improves one-class classification by incorporating reference vectors, with connections to approximate kernels and random mappings.

Findings

Improved classification accuracy on small-scale datasets

Kernel regularization and rank adjustment techniques

Enhanced incorporation of additional information into kernels

Abstract

In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities to approximate kernels, random mappings, and Non-linear Projection Trick. Focusing on small-scale one-class classification, our analysis and experimental results show that the new formulation provides approaches to regularize, adjust the rank, and incorporate additional information into the kernel itself, leading to improved one-class classification accuracy.