PIF: Anomaly detection via preference embedding

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.

@article{leveni2025_2505.10441,
  title={ PIF: Anomaly detection via preference embedding },
  author={ Filippo Leveni and Luca Magri and Giacomo Boracchi and Cesare Alippi },
  journal={arXiv preprint arXiv:2505.10441},
  year={ 2025 }
}