PIF: Anomaly detection via preference embedding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916738207055872 |
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| author | Leveni, Filippo Magri, Luca Boracchi, Giacomo Alippi, Cesare |
| author_facet | Leveni, Filippo Magri, Luca Boracchi, Giacomo Alippi, Cesare |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PIF: Anomaly detection via preference embedding Leveni, Filippo Magri, Luca Boracchi, Giacomo Alippi, Cesare Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | PIF: Anomaly detection via preference embedding |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.10441 |