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Local projections for high-dimensional outlier detection

Author

Listed:
  • Thomas Ortner

    (Vienna University of Technology)

  • Peter Filzmoser

    (Vienna University of Technology)

  • Maia Rohm

    (Vienna University of Technology)

  • Sarka Brodinova

    (Vienna University of Technology)

  • Christian Breiteneder

    (Vienna University of Technology)

Abstract

A novel approach for outlier detection is proposed, called local projections, which is based on concepts of the Local Outlier Factor (LOF) (Breunig et al. in Lof: identifying density-based local outliers. In: ACM sigmod record, ACM, volume 29, pp. 93–104, 2000) and ROBPCA (Hubert et al. in Technometrics 47(1):64–79, 2005). By using aspects of both methods, this algorithm is robust towards noise variables and is capable of performing outlier detection in multi-group situations. The idea is to focus on local descriptions of the observations and their neighbors using linear projections. The outlyingness of an observation is determined by a weighted distance of the observation to all identified projection spaces, with weights depending on the appropriateness of the local description. Experiments with simulated and real data demonstrate the usefulness of this method when compared to existing outlier detection algorithms.

Suggested Citation

  • Thomas Ortner & Peter Filzmoser & Maia Rohm & Sarka Brodinova & Christian Breiteneder, 2021. "Local projections for high-dimensional outlier detection," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 189-206, August.
    • Handle: RePEc:spr:metron:v:79:y:2021:i:2:d:10.1007_s40300-020-00183-5
    DOI: 10.1007/s40300-020-00183-5
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    References listed on IDEAS

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    1. Hubert, Mia & Van Driessen, Katrien, 2004. "Fast and robust discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 301-320, March.
    2. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
    3. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.
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