Generalized Reference Kernel for One-class Classification
This work addresses the need for better kernel methods in one-class classification, particularly for small-scale applications, though it appears incremental as it builds on existing kernel techniques.
The authors tackled the problem of improving one-class classification accuracy by formulating a new generalized reference kernel that regularizes, adjusts rank, and incorporates additional information into the kernel, leading to enhanced performance in small-scale settings.
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.