SOURCE-LINKED INTELLIGENCE
High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform m
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T08:42:27.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.