用随机剖分空间检测异常，速度快且不依赖参数调优。
Stochastic Voronoi Ensembles for Anomaly Detection
- 将数据空间随机划分为若干区域，独立分析每个区域内的异常点。
- 在45个数据集上优于12种主流方法，计算复杂度为线性。
- 适合处理密度不均的数据，无需手动调参，可直接用于工业质检。
异常检测旨在识别与多数数据显著偏离的样本，广泛应用于欺诈检测、网络安全和工业质量控制。现有方法在局部密度变化大的数据上表现不佳：基于距离的方法会漏检局部异常，而基于密度的方法需精细调参且时间复杂度达二次方。我们观察到，局部异常在全局分析中难以察觉，但在将数据空间分解为若干受限区域后，单独分析每个区域时会变得明显。基于此几何洞察，我们提出SVEAD（Stochastic Voronoi Ensembles Anomaly Detector），通过构建随机生成的维诺图集合，利用归一化单元相对距离并结合局部尺度加权来评分点。该方法实现线性时间复杂度和常数空间复杂度。在45个数据集上的实验表明，SVEAD优于12种当前最优方法。
原文摘要 · Abstract (English)
Anomaly detection aims to identify data instances that deviate significantly from majority of data, which has been widely used in fraud detection, network security, and industrial quality control. Existing methods struggle with datasets exhibiting varying local densities: distance-based methods miss local anomalies, while density-based approaches require careful parameter selection and incur quadratic time complexity. We observe that local anomalies, though indistinguishable under global analysis, become conspicuous when the data space is decomposed into restricted regions and each region is examined independently. Leveraging this geometric insight, we propose SVEAD (Stochastic Voronoi Ensembles Anomaly Detector), which constructs ensemble random Voronoi diagrams and scores points by normalized cell-relative distances weighted by local scale. The proposed method achieves linear time complexity and constant space complexity. Experiments on 45 datasets demonstrate that SVEAD outperforms 12 state-of-the-art approaches.
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