论文标题
图表上的采样信号：从理论到应用
Sampling Signals on Graphs: From Theory to Applications
论文作者
论文摘要
最近对图表上的采样信号进行了研究，目的是在当时和空间域中构建标准信号的采样类似物，最近引起了相当大的关注。除了增加图形信号处理（GSP）的不断增长的理论外，图上的采样还具有各种有希望的应用。在本文中，我们回顾了针对理论和潜在应用的图表进行采样的当前进度。尽管在图形信号采样中使用的大多数方法旨在与标准信号采样中使用的方法平行，但图形信号的采样理论与香农理论明显不同 - nyquist和shift-shift-nvarisiant采样。这部分是由于以下事实：在GSP系统中，几种重要属性的定义（例如偏移不变性和频率）是不同的。在整个综述中，我们讨论了标准信号和图形信号采样之间的相似性和差异，并突出了开放问题和挑战。
The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review current progress on sampling over graphs focusing on theory and potential applications. Although most methodologies used in graph signal sampling are designed to parallel those used in sampling for standard signals, sampling theory for graph signals significantly differs from the theory of Shannon--Nyquist and shift-invariant sampling. This is due in part to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. Throughout this review, we discuss similarities and differences between standard and graph signal sampling and highlight open problems and challenges.
