تقرير
Unmixing Noise from Hawkes Process to Model Learned Physiological Events
| العنوان: | Unmixing Noise from Hawkes Process to Model Learned Physiological Events |
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| المؤلفون: | Staerman, Guillaume, Loison, Virginie, Moreau, Thomas |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Physiological signal analysis often involves identifying events crucial to understanding 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 their parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.16938 |
| رقم الأكسشن: | edsarx.2406.16938 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |