Distance metric learning for conditional anomaly detection
For practitioners needing to detect anomalies conditioned on context, this work offers a tailored metric learning approach, but it is incremental as it adapts existing metric learning to a specific anomaly detection framework.
The paper addresses the problem of conditional anomaly detection, where anomalies depend on a subset of attributes, and proposes a metric learning method to optimize instance-based detection. The learned distance metric improves detection performance, though no specific numerical results are provided.
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.