This paper presents a new algorithm for reconstructing time-varying graph signals using spatiotemporal feature learning. We introduce a time series analysis method to capture the temporal stationarity of graph signals and propose a reconstruction model based on spatiotemporal coupling features. However, prior knowledge of the temporal stationarity of graph signals is required for this task. To address the issue, we analyze the spatio-temporal coupling feature in a graph-spectral view and design a learning framework to capture only the temporal feature based on the sampled signals. We reconstruct the graph signals using the proposed reconstruction model and feature-learning framework by solving an unconstrained optimization problem consisting of data fidelity and two regularization terms. Numerical results using both synthetic and real-world datasets demonstrate the superiority of the proposed reconstruction algorithm over existing methods.

中文翻译：

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基于时空特征学习的图信号重构

本文提出了一种利用时空特征学习重建时变图信号的新算法。我们引入了一种时间序列分析方法来捕获图信号的时间平稳性，并提出了一种基于时空耦合特征的重建模型。然而，此任务需要先验了解图信号的时间平稳性。为了解决这个问题，我们在图谱视图中分析时空耦合特征，并设计一个学习框架来仅捕获基于采样信号的时间特征。我们通过解决由数据保真度和两个正则化项组成的无约束优化问题，使用所提出的重建模型和特征学习框架来重建图信号。使用合成数据集和真实数据集的数值结果证明了所提出的重建算法相对于现有方法的优越性。