An Extreme Function Theory for Novelty Detection
An Extreme Function Theory for Novelty Detection
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DOI：
10.1109/jstsp.2012.2234081
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发表时间：
2013-02
影响因子：
7.5
通讯作者：
D. Clifton;Lei A. Clifton;Samuel Hugueny;David Wong;L. Tarassenko
中科院分区：
文献类型：
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作者：
D. Clifton;Lei A. Clifton;Samuel Hugueny;David Wong;L. Tarassenko
我们引入了一个极端的函数理论作为一种新的方法，通过该方法可以进行概率新奇检测的功能，其中的功能表示的时间序列（潜在的多变量）离散观测。我们设置的框架内的高斯过程（GP），这提供了一个方便的方法来构建一个分布函数的方法。而传统的新奇检测方法的目的是识别个别极端的数据点，相对于使用“正常”数据点的例子构建的正态性模型，所提出的方法的目的是识别极端函数，相对于使用“正常”函数的例子构建的正态性模型，其中这些函数表示的时间序列的观察。使用合成数据，从大型临床试验中获得的生理数据和基准时间序列数据集说明了该方法。
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.