Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 38, Issue 3, Pages 717-741Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-013-0685-z
Keywords
Gaussian processes; Extreme value theory; Novelty detection; Hellinger distance; Nonnegative matrix factorisation; Maritime traffic; Outlier detection
Funding
- ISSG, Babcock Marine and Technology Division, Devonport Royal Dockyard
- Microsoft Research
- EPSRC [EP/I011587/1]
- EPSRC [EP/I011587/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I011587/1] Funding Source: researchfish
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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.
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