通过偏好空间隔离检测结构异常，提升效率与精度。
Preference Isolation Forest for Structure-based Anomaly Detection
- 将数据嵌入高维偏好空间，利用流形拟合识别异常点。
- 三种隔离方法实现高效检测，其中滑动版在保持精度下提速显著。
- 适合需要高鲁棒性异常检测的工业场景或复杂数据建模。
我们研究基于低维流形表示的结构化模式异常检测问题。为此，提出一种通用框架——偏好隔离森林（Preference Isolation Forest, PIF），融合自适应隔离方法与偏好嵌入的灵活性。核心思想是通过拟合低维流形将数据嵌入高维偏好空间，并将异常视为孤立点。提出三种隔离策略：i) Voronoi-iForest，最通用方案；ii) RuzHash-iForest，利用局部敏感哈希避免显式距离计算；iii) Sliding-PIF，引入局部性先验以提升效率与效果。
原文摘要 · Abstract (English)
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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