Related papers: Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation

Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation

URL: http://arxiv.org/abs/2301.03011v2
Date: Thu, 12 Jan 2023 08:51:25 GMT
Title: Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Authors: Alejandro de la Concha and Argyris Kalogeratos and Nicolas Vayatis
Abstract summary: 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 $tau$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their associated node streams. We propose a novel kernel-based method to both detect $tau$ 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.
Score: 77.81487285123147
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 $\tau$, 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 $\tau$ 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.