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Knowledge and Information Systems

, Volume 38, Issue 3, pp 717–741 | Cite as

Maritime abnormality detection using Gaussian processes

  • Mark Smith
  • Steven Reece
  • Stephen Roberts
  • Ioannis Psorakis
  • Iead Rezek
Regular Paper

Abstract

Novelty, or abnormality, detection aims to identify patterns within data streams that do not conform to expected behaviour. This paper introduces novelty detection techniques using a combination of Gaussian processes, extreme value theory and divergence measurement to identify anomalous behaviour in both streaming and batch data. The approach is tested on both synthetic and real data, showing itself to be effective in our primary application of maritime vessel track analysis.

Keywords

Gaussian processes Extreme value theory Novelty detection  Hellinger distance Nonnegative matrix factorisation Maritime traffic Outlier detection 

Notes

Acknowledgments

This work was funded by ISSG, Babcock Marine and Technology Division, Devonport Royal Dockyard. Ioannis Psorakis is funded from a grant via Microsoft Research, for which we are most grateful. This work was further supported by EPSRC project EP/I011587/1.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Mark Smith
    • 1
  • Steven Reece
    • 2
  • Stephen Roberts
    • 2
  • Ioannis Psorakis
    • 2
  • Iead Rezek
    • 3
  1. 1.ISSG, Babcock Marine and Technology DivisionDevonport Royal DockyardPlymouthUK
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Schlumberger ResearchCambridgeUK

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