تقرير
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
| العنوان: | Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation |
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| المؤلفون: | de la Concha, Alejandro, Kalogeratos, Argyris, Vayatis, Nicolas |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2301.03011 |
| رقم الأكسشن: | edsarx.2301.03011 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |