Reproduction of scan B-statistic for kernel change-point detection algorithm

TL;DR

This paper reproduces and evaluates a kernel-based scan B-statistic for online change-point detection, demonstrating its superior performance over parametric methods and exploring subsampling improvements.

Contribution

It reproduces a recent kernel-based change-point detection algorithm and compares its effectiveness with parametric methods in online scenarios.

Findings

Scan B-statistic outperforms parametric methods in detection accuracy.

The algorithm detects changes more reliably in challenging scenarios.

Subsampling provides modest performance improvements.

Abstract

Change-point detection has garnered significant attention due to its broad range of applications, including epidemic disease outbreaks, social network evolution, image analysis, and wireless communications. In an online setting, where new data samples arrive sequentially, it is crucial to continuously test whether these samples originate from a different distribution. Ideally, the detection algorithm should be distribution-free to ensure robustness in real-world applications. In this paper, we reproduce a recently proposed online change-point detection algorithm based on an efficient kernel-based scan B-statistic, and compare its performance with two commonly used parametric statistics. Our numerical experiments demonstrate that the scan B-statistic consistently delivers superior performance. In more challenging scenarios, parametric methods may fail to detect changes, whereas the scan B-statistic successfully identifies them in a timely manner. Additionally, the use of subsampling techniques offers a modest improvement to the original algorithm.