Multi-scale sparse coding with anomaly detection and classification

Abstract

We here place a recent joint anomaly detection and classification approach based on sparse error coding methodology into multi-scale wavelet basis framework. The model is extended to incorporate an overcomplete wavelet basis into the dictionary matrix whereupon anomalies at specified multiple levels of scale are afforded equal importance. This enables, for example, subtle transient anomalies at finer scales to be detected which would otherwise be drowned out by coarser details and missed by the standard sparse coding techniques. Anomaly detection in power networks provides a motivating application and tests on a real-world data set corroborates the efficacy of the proposed model.