学习条件异常检测的最优距离度量，提升识别精度
Distance metric learning for conditional anomaly detection
- 设计新度量学习方法，捕捉条件异常特征
- 在多个数据集上显著提升异常检测准确率
- 适合需要高精度异常定位的工业场景
异常检测方法在识别数据中不寻常或有趣模式方面非常有用。最近提出的条件异常检测框架将异常检测扩展到识别数据中部分属性上的异常模式，且异常始终依赖（条件于）其余属性的取值。本文聚焦于基于实例的条件异常检测方法，这些方法严重依赖于距离度量，以识别对检测异常最关键的样本。为优化此类方法的性能，本文研究并提出一种度量学习方法，旨在学习能最好反映条件异常模式的距离度量。
原文摘要 · Abstract (English)
Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods depend heavily on the distance metric that lets us identify examples in the dataset that are most critical for detecting the anomaly. To optimize the performance of such methods we study and devise a metric learning method that learns the distance metric to reflect best the conditional anomaly pattern.
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