Dimensionality-Aware Outlier Detection

Alastair Anderberg, James Bailey, Ricardo J.G.B. Campello, Michael E. Houle, Henrique O. Marques, Miloš Radovanović, Arthur Zimek

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
EditorsShashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, Matteo Riondato
Number of pages9
PublisherSociety for Industrial and Applied Mathematics
Publication date2024
ISBN (Electronic)9781611978032
Publication statusPublished - 2024
Event2024 SIAM International Conference on Data Mining, SDM 2024 - Houston, United States
Duration: 18. Apr 202420. Apr 2024


Conference2024 SIAM International Conference on Data Mining, SDM 2024
Country/TerritoryUnited States

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Copyright © 2024 by SIAM.


  • intrinsic dimensionality
  • outlier detection


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