We present a nonparametric method for outlier detection that takes full
account of local variations in intrinsic dimensionality within the dataset.
Using the theory of Local Intrinsic Dimensionality (LID), our
'dimensionality-aware' outlier detection method, DAO, is derived as an
estimator of an asymptotic local expected density ratio involving the query
point and a close neighbor drawn at random. The dimensionality-aware behavior
of DAO is due to its use of local estimation of LID values in a
theoretically-justified way. Through comprehensive experimentation on more than
800 synthetic and real datasets, we show that DAO significantly outperforms
three popular and important benchmark outlier detection methods: Local Outlier
Factor (LOF), Simplified LOF, and kNN.

我们提出了一种非参数异常检测方法，该方法完全考虑了数据集中的局部内在维度变化。通过使用局部内在维度估计值的局部估计以理论上合理的方式，我们的维度感知异常检测方法 DAO 被推导为涉及查询点和随机选择的近邻之间的渐近本地期望密度比的估计器。通过对 800 多个合成和真实数据集的全面实验，我们表明 DAO 明显优于三个流行且重要的基准异常检测方法：局部异常因子（LOF），简化 LOF 和 kNN。