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.
翻译:在本文中,我们制定了一个新的通用参考内核,希望用一套参考矢量来改进原始基内核。根据选定的参考矢量,我们的配方显示了与近似内核、随机绘图和非线性投影陷阱的相似之处。 侧重于小规模的单级分类,我们的分析和实验结果显示,新配方提供了规范、调整等级和将额外信息纳入内核本身的方法,从而提高了单级分类的准确性。