论文标题
随时间变化的图形学习在图形时间变化上有限制
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
论文作者
论文摘要
我们提出了一个新的框架，用于从时空测量值学习时变图。给定信号的时间行为的适当先验，我们提出的方法可以从少量可用测量值中估算时间变化的图。为了实现这一目标，我们在凸优化问题中介绍了两个正则化项，从而限制了时间变化网络的时间变化的稀疏性。此外，引入了一种计算算法，以有效地解决优化问题。合成和真实数据集（点云和温度数据）的实验结果表明，我们所提出的方法的表现优于现有的最新方法。
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of the time-varying networks. Moreover, a computationally-scalable algorithm is introduced to efficiently solve the optimization problem. The experimental results with synthetic and real datasets (point cloud and temperature data) demonstrate our proposed method outperforms the existing state-of-the-art methods.
