ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2110.10518
16
0

Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes

20 October 2021
Alejandro de la Concha
Argyris Kalogeratos
Nicolas Vayatis
ArXiv (abs)PDFHTML
Abstract

Consider a heterogeneous data stream being generated by the nodes of a graph. The data stream is in essence composed by multiple streams, possibly of different nature that depends on each node. At a given moment τ\tauτ, a change-point occurs for a subset of nodes CCC, signifying the change in the probability distribution of their associated streams. In this paper we propose an online non-parametric method to infer τ\tauτ based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distribution associated with the data stream of each node. We propose a kernel-based method, under the hypothesis that connected nodes of the graph are expected to have similar likelihood-ratio estimates when there is no change-point. We demonstrate the quality of our method on synthetic experiments and real-world applications.

View on arXiv
Comments on this paper