An extreme function theory for novelty detection

Journal article

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.

Authors: Clifton, D; Clifton, L; Hugueny, S; Wong, D; Tarassenko, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Journal of selected topics in signal processing
• Volume: 7
• Issue: 1
• Pages: 28-37
• Publication date: 2012-12-13
• DOI: 10.1109/JSTSP.2012.2234081
• EISSN: 1941-0484
• ISSN: 1932-4553
• Keywords: Gaussian processes; functional analysis; signal processing algorithms