Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes

2023/12/05 by Hao Zhao, Zhao, Hao, Rong Pan +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Metabolomics and Mass Spectrometry Studies

paper · pdf · doi:10.48550/arxiv.2312.03176

openalex publication_date 2023/12/05 · openalex created_date 2023/12/08 · openalex updated_date 2026/07/28

Abstract

Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition in CPD, we introduce the Derivative-Aware Change Detection (DACD) method. It leverages the derivative process of a Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point locations effectively. DACD balances the exploitation and exploration of derivative processes through multiple data acquisition functions (AFs). By utilizing GP derivative mean and variance as criteria, DACD sequentially selects the next sampling data point, thus enhancing algorithmic efficiency and ensuring reliable and accurate results. We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.

Cited by

• Human Digital Twin: Data, Models, Applications, and Challenges

Related

• Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes
• Deep learning model solves change point detection for multiple change types
• Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
• Memory-free Online Change-point Detection: A Novel Neural Network Approach
• Learning Sinkhorn divergences for supervised change point detection