Robust Anomaly Detection with Applications to Acoustics and Graphs

There are four sorts of men: He who knows not and knows not that he knows not: He is a fool—shun him; He who knows not and knows that he knows not: He is simple—teach him; He who knows and knows not that he knows: He is asleep—wake him; He who knows and knows that he knows: He is wise—follow him. —Arabic proverb Our goal is to develop a robust anomaly detector that can be incorporated into pattern recognition systems that may need to be taught, but will never be shunned. The ability to know what we do not know is a concept often overlooked when developing classifiers to discriminate between different types of normal data in controlled experiments. We believe that an anomaly detector should be used to produce warnings in real applications when operating conditions change dramatically, especially when other classifiers only have a fixed set of bad candidates from which to choose. Our approach to distributional anomaly detection is to gather local information using features tailored to the domain, aggregate all such evidence to form a global ii