Dimensionality-Aware Outlier Detection: Theoretical and Experimental
  Analysis
Alastair Anderberg James Bailey Ricardo J. G. B. Campello Michael E. Houle Henrique O. Marques Miloš Radovanović Arthur Zimek
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.