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arXiv · 2505.10876

Preference Isolation Forest for Structure-based Anomaly Detection

Filippo Leveni·Luca Magri·Cesare Alippi·Giacomo Boracchi
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Abstract

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.

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Filippo Leveni, Luca Magri, Cesare Alippi, Giacomo Boracchi. 2025-05-16. Preference Isolation Forest for Structure-based Anomaly Detection. https://doi.org/10.1016/j.patcog.2025.112405

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