UNHaP: Unmixing Noise from Hawkes Process to Model Learned Physiological Events
Published in ICML AI4Science Workshop, 2024
Physiological signal analysis often involves identifying events crucial to understand the underlying biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustness, and the need for extensive labeled data. Data-driven methods like Convolutional Dictionary Learning (CDL) offer an alternative but tend to produce spurious detections. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. Leveraging marked Hawkes processes, UNHaP distinguishes between events of interest and spurious ones. By treating the event detection output as a mixture of structured and unstructured events, UNHaP efficiently unmixes these processes and estimates Hawkes process parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates.
Recommended citation: Staerman, G., Loison, V., & Moreau, T. (2024). Unmixing Noise from Hawkes Process to Model Learned Physiological Events. arXiv preprint arXiv:2406.16938.
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