# PIF: Anomaly detection via preference embedding

arXiv: https://arxiv.org/abs/2505.10441 · HTML: https://mlsanity.com/paper/2505.10441/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 1 of 1)

- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)

## Built on by (most cited first, 2 of 2)

- [Hashing for Structure-based Anomaly Detection](https://mlsanity.com/paper/2505.10873/) (2025)
- [Preference Isolation Forest for Structure-based Anomaly Detection](https://mlsanity.com/paper/2505.10876/) (2025)

## Abstract (arXiv)

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.