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

arXiv · 2401.05453

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

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Abstract

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.

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Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello, Michael E. Houle, Henrique O. Marques, Miloš Radovanović, Arthur Zimek. 2024-01-10. Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis. https://arxiv.org/abs/2401.05453

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