Preference Isolation Forest for Structure-based Anomaly Detection

We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general anomaly detection framework called Preference Isolation Forest (PIF), that combines the benefits of adaptive isolation-based methods with the flexibility of preference embedding. The key intuition is to embed the data into a high-dimensional preference space by fitting low-dimensional manifolds, and to identify anomalies as isolated points. We propose three isolation approaches to identify anomalies: $i$) Voronoi-iForest, the most general solution, $ii$) RuzHash-iForest, that avoids explicit computation of distances via Local Sensitive Hashing, and $iii$) Sliding-PIF, that leverages a locality prior to improve efficiency and effectiveness.

@article{leveni2025_2505.10876,
  title={ Preference Isolation Forest for Structure-based Anomaly Detection },
  author={ Filippo Leveni and Luca Magri and Cesare Alippi and Giacomo Boracchi },
  journal={arXiv preprint arXiv:2505.10876},
  year={ 2025 }
}