2023/01/08 by Alejandro de la Concha, de la Concha, Alejandro, Argyris Kalogeratos +3
Computer Science · Mathematics · #Data Stream Mining Techniques #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2301.03011
openalex publication_date 2023/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point τ, a change occurs at a subset of nodes C, which affects the probability distribution of their associated node streams. In this paper, we propose a novel kernel-based method to both detect τ and localize C, based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distributions of the node streams. Our main working hypothesis is the smoothness of the likelihood-ratio estimates over the graph, i.e connected nodes are expected to have similar likelihood-ratios. The quality of the proposed method is demonstrated on extensive experiments on synthetic scenarios.