Anomaly Detection Using Principal Component Analysis

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.