Outlier Detection at GBIF Using DBSCAN

Outlier Detection Using SemiDiscrete Decomposition

Semidiscrete decomposition (SDD) is usually presented as a storage-eecient analogue of singular value decomposition. We show, however, that SDD actually works in a completely diierent way, and is best thought of as a bump-hunting technique; it is extremely eeective at nding outlier clusters in datasets. We suggest that SDD's success in text retrieval applications such as latent semantic indexin...

Outlier Detection Using Default Logic

Default logic is used to describe regular behavior and normal properties. We suggest to exploit the framework of default logic for detecting outliers individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledgebase integrity and to single out irregular individuals. We first formally define the notion of an outlier and ...

Outlier Detection using Graph Mining

Outlier Detection Using Classifier Instability

When a classiier is used to classify objects, it is important to know if these objects resemble the train objects the classiier is trained with. Several methods to detect novel objects exist. In this paper a new method is presented which is based on the instability of the output of simple classiiers on new objects. The performances of the outlier detection methods is shown in a handwritten digi...

Multivariate Outlier Detection Using Independent Component Analysis

The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection , data clustering and multivariate data visualization etc . Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be ...