# Hashing for Structure-based Anomaly Detection

arXiv: https://arxiv.org/abs/2505.10873 · HTML: https://mlsanity.com/paper/2505.10873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 3 of 3)

- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Functional Isolation Forest](https://mlsanity.com/paper/1904.04573/) (2019)
- [PIF: Anomaly detection via preference embedding](https://mlsanity.com/paper/2505.10441/) (2025)

## Abstract (arXiv)

We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.