一种无需假设分布、不依赖难调超参的高维异常检测新方法
Kernel Outlier Detection
- 先用核变换再通过投影寻踪搜索异常方向
- 在三个小数据集和四个大数据集上均表现优异
- 适合对鲁棒性要求高的高维异常检测场景
提出一种名为核异常检测（Kernel Outlier Detection, KOD）的新异常检测方法，旨在解决高维数据中的异常检测挑战。该方法克服了现有方法对分布假设的依赖及难以调节的超参数问题。KOD首先进行核变换，随后采用投影寻踪策略。其创新点在于引入了一种新的方向搜索集合，以及一种融合不同方向类型结果的新方式，从而实现灵活且轻量的异常检测。实验评估表明，KOD在三个结构复杂的小型数据集和四个大型基准数据集上均表现出有效性。
原文摘要 · Abstract (English)
A new anomaly detection method called kernel outlier detection (KOD) is proposed. It is designed to address challenges of outlier detection in high-dimensional settings. The aim is to overcome limitations of existing methods, such as dependence on distributional assumptions or on hyperparameters that are hard to tune. KOD starts with a kernel transformation, followed by a projection pursuit approach. Its novelties include a new ensemble of directions to search over, and a new way to combine results of different direction types. This provides a flexible and lightweight approach for outlier detection. Our empirical evaluations illustrate the effectiveness of KOD on three small datasets with challenging structures, and on four large benchmark datasets.
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