用偏好嵌入与树模型结合，高效识别复杂模式中的异常点。
PIF: Anomaly detection via preference embedding
- 将数据嵌入高维偏好空间，用树结构计算异常得分。
- 在合成与真实数据集上优于现有主流方法。
- 适合检测具有复杂结构的异常，如金融欺诈、设备故障。
我们针对结构化模式中的异常检测问题提出一种新方法PIF，融合自适应隔离与偏好嵌入的优势。具体地，将数据嵌入高维空间，利用基于树的高效算法PI-Forest计算异常分数。在合成与真实数据集上的实验表明，PIF显著优于当前主流异常检测技术，且证明了PI-Forest在偏好空间中更擅长测量任意距离并有效隔离异常点。
原文摘要 · Abstract (English)
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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