Novelty detection involves the construction of a "model of normality", and then classifies test data as being either "normal" or "abnormal" with respect to that model. For this reason, it is often termed one-class classification. The approach is suitable for cases in which examples of "normal" behaviour are commonly available, but in which cases of "abnormal" data are comparatively rare. When performing novelty detection, we are typically most interested in the tails of the normal model, because it is in these tails that a decision boundary between "normal" and "abnormal" areas of data space usually lies. Extreme value statistics provides an appropriate theoretical framework for modelling the tails of univariate (or low-dimensional) distributions, using the generalised Pareto distribution (GPD), which can be demonstrated to be the limiting distribution for data occurring within the tails of most practically-encountered probability distributions. This paper provides an extension of the GPD, allowing the modelling of probability distributions of arbitrarily high dimension, such as occurs when using complex, multimodel, multivariate distributions for performing novelty detection in most real-life cases. We demonstrate our extension to the GPD using examples from patient physiological monitoring, in which we have acquired data from hospital patients in large clinical studies of high-acuity wards, and in which we wish to determine "abnormal" patient data, such that early warning of patient physiological deterioration may be provided.

中文翻译：

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扩展用于高维空间中的新颖性检测的广义帕累托分布。


新颖性检测涉及构建“正常性模型”，然后根据该模型将测试数据分类为“正常”或“异常”。因此，它通常被称为一类分类。该方法适用于“正常”行为示例普遍存在但“异常”数据相对较少的情况。在执行新颖性检测时，我们通常对正常模型的尾部最感兴趣，因为数据空间的“正常”和“异常”区域之间的决策边界通常位于这些尾部。极值统计为单变量（或低维）分布的尾部建模提供了一个合适的理论框架，使用广义帕累托分布（GPD），它可以被证明是大多数实际情况中发生在尾部的数据的限制分布。遇到概率分布。本文提供了 GPD 的扩展，允许对任意高维的概率分布进行建模，例如在大多数现实生活中使用复杂、多模型、多变量分布执行新颖性检测时发生的情况。我们使用患者生理监测的例子来展示我们对 GPD 的扩展，其中我们在高危病房的大型临床研究中从医院患者那里获取了数据，并且我们希望确定“异常”患者数据，以便早期预警可以提供患者生理恶化的情况。