用混合方法检测粒子运动变化点并估计扩散参数。
CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data
- 融合神经网络、特征机器学习与统计方法进行轨迹分析。
- 在异常扩散挑战赛中表现优异，可准确识别运动模式转变点。
- 适合研究软物质与生物物理中粒子运动行为的科研人员。
变化点检测已成为单粒子追踪数据分析的重要环节，能够识别粒子运动模式发生显著变化的时间点。基于这些时间点对扩散轨迹进行分割，有助于揭示软凝聚态与生物物理中的多种现象。本文提出CINNAMON，一种混合方法，用于分类单粒子追踪轨迹、检测轨迹内的变化点，并估计变化点之间的扩散参数。该方法结合了神经网络、基于特征的机器学习与统计技术，在第二届异常扩散挑战赛中进行了基准测试。其分析与特征组件提供了高度可解释性。文中还探讨了拓扑数据分析特征的潜在应用。
原文摘要 · Abstract (English)
Change point detection has become an important part of the analysis of the single-particle tracking data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter and biological physics. In this paper, we propose CINNAMON, a hybrid approach to classifying single-particle tracking trajectories, detecting change points within them, and estimating diffusion parameters in the segments between the change points. Our method is based on a combination of neural networks, feature-based machine learning, and statistical techniques. It has been benchmarked in the second Anomalous Diffusion Challenge. The method offers a high level of interpretability due to its analytical and feature-based components. A potential use of features from topological data analysis is also discussed.
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