Online change-point detection with kernels

作者：

Highlights：

• Nonparametric change detection.

• Computationally efficient online change-point detection.

• Theoretical analysis of the online algorithm convergence.

• Detection performances comparable to less computationally efficient reference algorithms.

摘要

•Nonparametric change detection.•Computationally efficient online change-point detection.•Theoretical analysis of the online algorithm convergence.•Detection performances comparable to less computationally efficient reference algorithms.

论文关键词：Non-parametric change-point detection,Reproducing kernel Hilbert space,Kernel least-mean-square algorithm,Online algorithm,Convergence analysis

论文评审过程：Received 16 July 2020, Revised 1 September 2022, Accepted 4 September 2022, Available online 7 September 2022, Version of Record 15 September 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109022