利用复合信号神经网络捕获和解释轨迹奇异点
Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks
摘要：
空间轨迹是无处不在的复杂信号。他们的分析在许多研究领域都至关重要，从城市规划到神经科学。已经提出了几种方法来聚类轨迹。它们依赖于手工制作的特征，这些特征难以捕捉信号的时空复杂性，或者依赖于人工神经网络（ANN），后者效率更高，但可解释性较差。在本文中，我们提出了一种新的人工神经网络架构，旨在捕捉一组轨迹的时空模式特性，同时考虑到人口统计的导航。因此，我们的模型提取了与行为和人口统计学相关的标记。我们提出了一个复合信号分析仪（CompSNN）结合三个简单的人工神经网络模块。这些模块中的每一个都使用轨迹的不同信号表示，同时保持可解释性。我们的CompSNN比孤立的模块表现得更好，并且可以可视化信号的哪些部分对区分轨迹最有用。
英文摘要：
Spatial trajectories are ubiquitous and complex signals. Their analysis is crucial in many research fields, from urban planning to neuroscience. Several approaches have been proposed to cluster trajectories. They rely on hand-crafted features, which struggle to capture the spatio-temporal complexity of the signal, or on Artificial Neural Networks (ANNs) which can be more efficient but less interpretable. In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators. Hence, our model extracts markers linked to both behaviour and demographics. We propose a composite signal analyser (CompSNN) combining three simple ANN modules. Each of these modules uses different signal representations of the trajectory while remaining interpretable. Our CompSNN performs significantly better than its modules taken in isolation and allows to visualise which parts of the signal were most useful to discriminate the trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
