Perspective-Based Multi-task Learning for Outlier Interpretation

Anomaly detection has been intensively studied in recent years, yet answering the reason why an instance is detected to be an outlier, i.e., outlier interpretation, remains challenging. To this end, existing model-agnostic methods mainly explain outliers by mining the feature subspace. However, most of them solely use normal instances to explain the query outlier. This single-perspective consideration often leads to a lack of specificity, as it fails to capture the nuanced distinctions needed to explain why the query outlier is different from other outliers. Furthermore, outliers similar to or different from the query outlier may share the same subspace for interpretation, resulting in explanations that lack targeted insights. To address these limitations, we propose a novel model-agnostic outlier interpretation method named PML (Perspective-based Multi-task Learning for outlier interpretation). Specifically, PML samples and transforms datasets into quintuplets and learns representations of quintuplets. Furthermore, PML introduces a multi-task learning strategy to learn the feature subspace from both normal and abnormal perspectives. Finally, PML uses the optimal feature subspace to explain why the query outlier is anomalous. Through extensive experiments, we demonstrate the effectiveness of PML. The source code is available at https://github.com/scu-kdde/OAM-PML-2025.