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Outlier, or anomaly, detection is essential for optimal performance ofmachine learning methods and statistical predictive models. It is not just atechnical step in a data cleaning process but a key topic in many fields suchas fraudulent document detection, in medical applications and assisteddiagnosis systems or detecting security threats. In contrast topopulation-based methods, neighborhood based local approaches are simpleflexible methods that have the potential to perform well in small sample sizeunbalanced problems. However, a main concern of local approaches is the impactthat the computation of each sample neighborhood has on the method performance.Most approaches use a distance in the feature space to define a singleneighborhood that requires careful selection of several parameters. This workpresents a local approach based on a local measure of the heterogeneity ofsample labels in the feature space considered as a topological manifold.Topology is computed using the communities of a weighted graph codifying mutualnearest neighbors in the feature space. This way, we provide with a set ofmultiple neighborhoods able to describe the structure of complex spaces withoutparameter fine tuning. The extensive experiments on real-world data sets showthat our approach overall outperforms, both, local and global strategies inmulti and single view settings.

A flexible outlier detector based on a topology given by graph communities