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An Extreme Function Theory for Novelty Detection

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5 Author(s)
Clifton, D.A. ; Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK ; Clifton, L. ; Hugueny, S. ; Wong, D.
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We introduce an extreme function theory as a novel method by which probabilistic novelty detection may be performed with functions, where the functions are represented by time-series of (potentially multivariate) discrete observations. We set the method within the framework of Gaussian processes (GP), which offers a convenient means of constructing a distribution over functions. Whereas conventional novelty detection methods aim to identify individually extreme data points, with respect to a model of normality constructed using examples of “normal” data points, the proposed method aims to identify extreme functions, with respect to a model of normality constructed using examples of “normal” functions, where those functions are represented by time-series of observations. The method is illustrated using synthetic data, physiological data acquired from a large clinical trial, and a benchmark time-series dataset.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:7 ,  Issue: 1 )