Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely advantageous to analyze such structured data as "signals on graphs" and thus benefit from the ability of graphs to incorporate spatial sensing awareness, physical intuition, and sensor importance, together with the inherent "local versus global" sensor association. The aim of this lecture note is, therefore, to establish a common language between graph signals that are observed in irregular signal domains and some of the most fundamental paradigms in digital signal processing (DSP), such as spectral analysis, system transfer function, digital filter design, parameter estimation, and optimal denoising.

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通过直观的示例驱动方法理解图信号处理的基础 [讲义]

图是不规则的结构，自然地代表了多方面的数据属性；然而，传统方法是在信号处理之外建立的，主要集中在分析底层图形而不是图形上的信号。鉴于多传感器和多节点测量的可用性迅速增加，可能记录在不规则或临时网格上，分析诸如“图上的信号”之类的结构化数据将非常有利，从而受益于图结合空间感知意识的能力，物理直觉和传感器重要性，以及固有的“局部与全局”传感器关联。因此，本讲义的目的是，