Distance metric learning for conditional anomaly detection

TL;DR

This paper introduces a metric learning approach tailored for conditional anomaly detection, enhancing the identification of anomalous patterns conditioned on specific attribute subsets.

Contribution

It proposes a novel metric learning method that optimizes distance metrics specifically for instance-based conditional anomaly detection.

Findings

The learned metric improves detection accuracy in conditional anomaly scenarios.

The method effectively captures complex attribute dependencies.

Experimental results demonstrate superior performance over baseline metrics.

Abstract

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.