论文标题
隔离分布内核：点和组异常检测的新工具
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection
论文作者
论文摘要
我们将隔离分布内核作为衡量两个分布之间的相似性的一种新方法。基于内核均值嵌入的现有方法将点内核转换为分布内核，有两个关键问题：使用的点内核具有一个具有棘手的维度的功能图；它是{\ em数据独立}。本文表明，基于{\ em数据依赖}点内核的隔离分布内核（IDK）解决了这两个关键问题。我们证明了IDK的功效和效率，作为对点和组异常的基于内核异常检测的新工具。没有明确的学习，单独使用IDK优于现有基于内核的点异常检测器OCSVM和其他内核表示依赖高斯内核的嵌入方法。对于组异常检测，我们引入了一个基于IDK的检测器，称为IDK $^2 $。它将输入空间中的群体异常检测到希尔伯特空间中的点异常检测问题，而无需学习。 IDK $^2 $运行的数量级要比组异常检测器OCSMm.M.我们首次揭示，基于内核基于内核的有效异常检测器基于内核平均值嵌入必须采用依赖数据依赖性的特征内核。
We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}. This paper shows that Isolation Distributional Kernel (IDK), which is based on a {\em data dependent} point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection for both point and group anomalies. Without explicit learning, using IDK alone outperforms existing kernel based point anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. For group anomaly detection,we introduce an IDK based detector called IDK$^2$. It reformulates the problem of group anomaly detection in input space into the problem of point anomaly detection in Hilbert space, without the need for learning. IDK$^2$ runs orders of magnitude faster than group anomaly detector OCSMM.We reveal for the first time that an effective kernel based anomaly detector based on kernel mean embedding must employ a characteristic kernel which is data dependent.
