Issue No. 05 - May (2015 vol. 27)
Milos Radovanovic , Faculty of Sciences, University of Novi Sad, Serbia
Alexandros Nanopoulos , Ingolstadt School of Management, University of Eichstaett-Ingolstadt, Germany
Mirjana Ivanovic , Faculty of Sciences, University of Novi Sad, Serbia
Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality.” A prevailing view is that distance concentration, i.e., the tendency of distances in high-dimensional data to become indiscernible, hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we provide evidence supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings. Furthermore, we show that high dimensionality can have a different impact, by reexamining the notion of reverse nearest neighbors in the unsupervised outlier-detection context. Namely, it was recently observed that the distribution of points’ reverse-neighbor counts becomes skewed in high dimensions, resulting in the phenomenon known as
hubness. We provide insight into how some points (antihubs) appear very infrequently in -NN lists of other points, and explain the connection between antihubs, outliers, and existing unsupervised outlier-detection methods. By evaluating the classic $_$k$_$ -NN method, the angle-based technique designed for high-dimensional data, the density-based local outlier factor and influenced outlierness methods, and antihub-based methods on various synthetic and real-world data sets, we offer novel insight into the usefulness of reverse neighbor counts in unsupervised outlier detection. $_$k$_$
Standards, Correlation, Euclidean distance, Context, Educational institutions, Noise measurement, Histograms
M. Radovanovic, A. Nanopoulos and M. Ivanovic, "Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 5, pp. 1369-1382, 2015.