Anomaly detection with dynamic density estimation

Abstract

In detecting anomaly in samples, convolutional neural network (CNN) and machine-learning classifier modelled with support vectors are used. The CNN and classifier are initially trained with normal samples, and incrementally trained in retraining sessions each with self-generated anomalous samples identified in inference preceding a retraining session under consideration, thereby continually improving the anomaly-detection performance without a need to seek anomalous samples for initializing the CNN and classifier. The support vectors are selected as feature k-centers of output feature map of the CNN. Dynamic density estimation is used to determine which feature k-centers in existing support vector set are retainable in updating the support vector set in the retraining session. As such, not all feature k-centers need to be recomputed to give the support vectors in the updated support vector set. Computation effort in updating the support vector set is reduced in comparison to generating this set from scratch.