基于图的离散信号处理
Discrete Signal Processing on Graphs
摘要：
在社会环境中，个人通过关系网进行互动。每个人都是一个复杂的网络（或图形）中的一个节点，并产生数据，大量的数据。我们通过数据的来源来标记数据，或者正式地说，我们通过图的节点来索引数据。所得到的信号（由节点索引的数据）与由良好排序的时间样本或像素索引的时间或图像信号相距甚远。数字信号处理（DSP），即离散信号处理，提供了一种全面、优雅、高效的方法来描述、表示、变换、分析、处理或合成这些有序的时间或图像信号。本文扩展到图形上的信号DSP及其基本原理，包括滤波器，卷积，z变换，脉冲响应，频谱表示，傅立叶变换，频率响应，并说明DSP的图形分类博客，线性预测和压缩数据从不规则的气象站，或预测的移动服务提供商的客户的行为。
英文摘要：
In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered time samples or pixels. DSP, discrete signal processing, provides a comprehensive, elegant, and efficient methodology to describe, represent, transform, analyze, process, or synthesize these well ordered time or image signals. This paper extends to signals on graphs DSP and its basic tenets, including filters, convolution, z-transform, impulse response, spectral representation, Fourier transform, frequency response, and illustrates DSP on graphs by classifying blogs, linear predicting and compressing data from irregularly located weather stations, or predicting behavior of customers of a mobile service provider.
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。
