From outliers to prototypes: Ordering data

Stefan Harmeling, Guido Dornhege, David Tax, Frank Meinecke, Klaus Robert Müller

Research output: Contribution to journalArticle

55 Citations (Scopus)

Abstract

We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.

Original languageEnglish
Pages (from-to)1608-1618
Number of pages11
JournalNeurocomputing
Volume69
Issue number13-15
DOIs
Publication statusPublished - 2006 Aug

Keywords

  • Clustering
  • Nearest neighbors
  • Noisy dimensionality reduction
  • Novelty detection
  • Ordering
  • Outlier detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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  • Cite this

    Harmeling, S., Dornhege, G., Tax, D., Meinecke, F., & Müller, K. R. (2006). From outliers to prototypes: Ordering data. Neurocomputing, 69(13-15), 1608-1618. https://doi.org/10.1016/j.neucom.2005.05.015