Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection
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DOI：
10.1145/3394486.3403062
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
K. Ting;Bi-Cun Xu;T. Washio;Zhi-Hua Zhou
中科院分区：
文献类型：
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作者：
K. Ting;Bi-Cun Xu;T. Washio;Zhi-Hua Zhou
我们引入隔离分布核作为一种新的方法来衡量两个分布之间的相似性。现有的方法基于核均值嵌入，将点核转换为分布核，有两个关键问题：所采用的点核具有难以处理的维数的特征映射;它是数据无关的。本文表明，隔离分布内核（IDK），这是基于数据依赖点内核，解决这两个关键问题。我们证明了IDK的效力和效率作为一种新的工具，基于内核的异常检测。在没有显式学习的情况下，单独使用IDK优于现有的基于核的异常检测器OCSVM和其他依赖于高斯核的核均值嵌入方法。我们首次揭示了基于核均值嵌入的有效的基于核的异常检测器必须采用依赖于数据的特征核。
We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which converts 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 data independent. This paper shows that Isolation Distributional Kernel (IDK), which is based on a data dependent point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection. Without explicit learning, using IDK alone outperforms existing kernel based anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. 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.