PIF: Anomaly detection via preference embedding
This addresses anomaly detection in structured data, but appears incremental as it builds on existing isolation and embedding methods.
The paper tackles anomaly detection for structured patterns by introducing PIF, a method that combines adaptive isolation with preference embedding, and shows it favorably compares with state-of-the-art techniques on synthetic and real datasets.
We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-Forest, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-Forest is better at measuring arbitrary distances and isolate points in the preference space.