Statistical modeling and inference in generalized graph signal processing - Jian, Xingchao (Nanyang Technological University, 2025)
In modern signal processing, one of the greatest challenges is the more and more complex domain structure of the signal. One typical structure is a graph, or network, where vertices represent entities and edges denote the relationships between them. For signals supported on the graph structures, the filtering, sampling and reconstruction techniques require proper use of the graph. Graph signal processing (GSP) has emerged as a powerful tool for addressing these problems by leveraging the domain knowledge of graphs. It extends the traditional signal processing domain from discrete-time stamps to vertices in a network. GSP has found wide applications in various domains, including point cloud denoising, image processing, and brain network analysis, etc.