Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
This addresses the problem of biased or irrelevant anomaly detection for operators in fields like security or healthcare, though it is incremental as it builds on existing density-based methods.
The paper tackles the ambiguity in anomaly detection where nuisance attributes like age or gender can be misinterpreted as anomalies, by introducing a method that allows operators to exclude such attributes from consideration, resulting in verified effectiveness through empirical investigation.
Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.g., age, race or gender, may be considered anomalous by some operators while others may consider this attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection. Our approach then learns representations which do not contain information over the nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes that are relevant for detecting anomalies, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach.