Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection

arXiv:2404.04064v1 Announce Type: cross
Abstract: We study in this paper the improvement of one-class support vector machines (OC-SVM) through sparse representation techniques for unsupervised anomaly detection. As Dictionary Learning (DL) became recently a common analysis technique that reveals hidden sparse patterns of data, our approach uses this insight to endow unsupervised detection with more control on pattern finding and dimensions. We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, …

analysis anomaly detection arxiv class cs.cr cs.lg cs.na data detection hidden improvement insight machines math.na patterns representation study support techniques