从时间-顶点谱学习图ARMA过程
Learning Graph ARMA Processes from Time-Vertex Spectra
AI总结：
提出一种从时间-顶点谱学习图ARMA过程的算法，通过估计并细化联合功率谱密度，实现高精度的时变图信号插值。
摘要：
将时变图信号建模为平稳的时间-顶点随机过程，可以通过有效利用该过程在不同图节点和时间实例上的相关模式来推断缺失的信号值。在本研究中，我们提出了一种计算图自回归移动平均（graph ARMA）过程的算法，该算法基于从信号的不完整实现中学习过程的联合时间-顶点功率谱密度，以完成信号插值任务。我们的解决方案首先从部分观测的实现中粗略估计过程的联合谱，然后通过凸松弛将该估计投影到图ARMA过程的谱流形上以进行细化。随后，基于学习到的模型估计最初缺失的信号值。实验结果表明，所提出的方法在时间-顶点信号估计问题中实现了高精度。
英文摘要：
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. Our solution relies on first roughly estimating the joint spectrum of the process from partially observed realizations and then refining this estimate by projecting it onto the spectrum manifold of the graph ARMA process through convex relaxations. The initially missing signal values are then estimated based on the learnt model. Experimental results show that the proposed approach achieves high accuracy in time-vertex signal estimation problems.
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。
