Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection

arXiv:2404.04064v1 Announce Type: new
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, …

abstract analysis anomaly anomaly detection arxiv class cs.cr cs.lg cs.na data detection dictionary hidden improvement insight machines math.na paper patterns representation study support support vector machines svm through type unsupervised vector