论文标题
一级分类的广义参考内核
Generalized Reference Kernel for One-class Classification
论文作者
论文摘要
在本文中，我们制定了一个新的广义参考内核，希望使用一组参考向量改善原始基础内核。根据所选的参考矢量，我们的公式显示了与近似内核，随机映射和非线性投影技巧的相似性。我们的分析和实验结果专注于小规模的一级分类，表明，新的配方提供了正规化，调整等级并将其他信息纳入内核本身的方法，从而提高了一类分类的精度。
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.
