Outlier Detection Using k-Nearest Neighbour Graph

By Ville Hautamäki, Ismo Kärkkäinen, Pasi Fränti. International Conference on Pattern Recognition, 2004

402 citations recorded by the source corpus.

How researchers see this paper

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Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018, background): “Distance based outlier detection methods [20, 17, 21] identify outliers by their distance to neighboring examples.”
An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007, methodology): “Several papers [76,77] have proposed using the k-nearest neighbor outlier detection algorithms for the purpose of anomaly detection.”
Generalized Out-of-Distribution Detection: A Survey (2021, neutral): “Another set of methods uses the relationship among data points and constructs a neighborhood graph [342], [343] or its variants [344].”
HRN: A Holistic Approach to One Class Learning (2020, background): “In general, anomaly detection algorithms can be classiﬁed into the following categories: distance based methods [42, 3, 28, 31, 60], density based methods [38, 56, 6], mixture models [3, 43], one-class classiﬁcation based methods [75, 78, 39], deep learning based representation learning using…”
Improving K-Means by Outlier Removal (2005, methodology): “Outlier Detection using Indegree Number (ODIN) [9] is a local density-based outlier detection algorithm.”