Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection

Abstract

Anomaly detection constitutes a critical field of research, concerned with the identification of rare, atypical, or unexpected patterns within a dataset. Within the existing literature, the majority of anomaly detection techniques lack the capability to localize the anomalies. Recently, techniques such as sparse anomaly decomposition methods possess the distinctive ability to not only detect but also pinpoint the location of the anomalies concurrently. In the subsequent sections of this chapter, an exhaustive review of existing anomaly decomposition techniques will be conducted, with a particular emphasis on the smooth sparse decomposition method. Following this, several contemporary extensions to sparse decomposition methods will be explored, resulting in a discussion on the prospective directions for future research in this domain.