PIF: Anomaly detection via preference embedding

• URL: http://arxiv.org/abs/2505.10441v1
• Date: Thu, 15 May 2025 16:00:31 GMT
• Title: PIF: Anomaly detection via preference embedding
• Authors: Filippo Leveni, Luca Magri, Giacomo Boracchi, Cesare Alippi,
• Abstract summary: We propose a novel anomaly detection method called PIF.<n>We embed the data in a high dimensional space where an efficient tree-based method, PI-Forest, is employed to compute an anomaly score.
• Score: 22.383337771018958
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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