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OFAI-TR-2016-10 ( 116kB PDF file)

An Empirical Analysis of Hubness in Unsupervised Distance-Based Outlier Detection

Arthur Flexer

Outlier detection is the task of automatic identification of unknown data not covered by training data (e.g. a previously unknown class in classification). We explore outlier detection in the presence of hubs and anti-hubs, i.e. data objects which appear to be either very close or very far from most other data due to a problem of measuring distances in high dimensions. We compare a classic distance based method to two new approaches, which have been designed to counter the negative effects of hubness, on six high-dimensional data sets. We show that mainly anti-hubs pose a problem for outlier detection and that this can be improved by using a hubness-aware approach based on re-scaling the distance space.

Keywords: Outlier detection, Hubness, Curse of dimensionality, Evaluation

Citation: Flexer A.: An Empirical Analysis of Hubness in Unsupervised Distance-Based Outlier Detection, in Proceedings of 4th International Workshop on High Dimensional Data Mining (HDM), in conjunction with the IEEE International Conference on Data Mining (IEEE ICDM 2016), Barcelona, Spain, 2016.