Unsupervised and Active Learning Using Maximin-Based Anomaly Detection

Abstract

Unsupervised anomaly detection is commonly performed using a distance or density based technique, such as K-Nearest neighbours, Local Outlier Factor or One-class Support Vector Machines. One-class Support Vector Machines reduce the computational cost of testing new data by providing sparse solutions. However, all these techniques have relatively high computational requirements for training. Moreover, identifying anomalies based solely on density or distance is not sufficient when both point (isolated) and cluster anomalies exist in an unlabelled training set. Finally, these unsupervised anomaly detection techniques are not readily adapted for active learning, where the training algorithm sho..