Anomaly Detection Using Principal Component Analysis
Anomaly Detection Using Principal Component Analysis
复制标题
使用主成分分析进行异常检测
DOI：
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发表时间：
2014
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通讯作者：
Adathakula Sree Deepthi
中科院分区：
文献类型：
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作者：
Adathakula Sree Deepthi
异常检测是对不符合预期模式或数据集中其他项目的项目、事件或观测结果的识别。通常，异常项目会转化为某种问题，比如银行欺诈、结构缺陷、医疗问题或文本中的错误。异常也被称为离群值、新奇事物、噪声、偏差和例外。许多用于检测离群值的技术从根本上是相同的，但作者选用了不同的名称。在最一般的情况下，异常检测器可以检测出与表征正常行为的既定基准轮廓的偏差。异常检测在各种各样的应用中广泛使用，比如信用卡、保险或医疗保健的欺诈检测，网络安全的入侵检测，安全关键系统的故障检测，以及对敌方活动的军事监视。异常检测的重要性在于数据中的异常在各种各样的应用领域会转化为重要的可操作信息。主成分分析（PCA）是一种统计方法，它使用正交变换将一组可能相关变量的观测值转换为一组线性不相关变量的值。在本文中，我们讨论了各种异常检测技术及其优缺点。
Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or finding errors in text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. Many techniques employed for detecting outliers are fundamentally identical but with different names chosen by the authors. In the most general case, an anomaly detector can detect deviations from an established baseline profile that characterizes normal behavior. Anomaly detection finds extensive use in a wide variety of applications such as fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, fault detection in safety critical systems, and military surveillance for enemy activities. The importance of anomaly detection is due to the fact that anomalies in data translate to significant actionable information in a wide variety of application domains. Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. In this paper we discuss various anomaly detection techniques and their merits and demerits.