广州数学大讲坛第四期
第三十一讲——中山大学成诚研究员学术报告
题目：Graph Fourier Transforms on Directed Graphs
时间：2024年5月25日（周六）下午15:35-16:35
地点：理学实验楼314
报告人：成诚研究员
摘要：Graph signal processing provides an innovative framework to process data on graphs. The widely used graph Fourier transform on the undirected graph is based on the eigen-decomposition of the Laplacian. In many engineering applications, the data is time-varying and pairwise interactions among agents of a network are not always mutual and equitable, such as the interaction data on a social network. Then the graph Fourier transform on directed graph is in demand and it should be designed to reflect the spectral characteristic for different directions, decompose graph signals into different frequency components, and to efficiently represent the graph signal by different modes of variation. In this talk, I will present our recent work on the graph Fourier transforms on directed graphs which are based on the singular value decompositions of the Laplacians..
报告人简介：现为中山大学学院特聘研究员。成诚的研究方向是应用调和分析，其在相位恢复和图信号的分布式处理以及采样理论等方面展开了系统的研究，截至目前共发表论文17篇，其中包括期刊论文14篇，包括Appl. Comput. Harmon. Anal.，J. Funct. Anal., J. Fourier Anal. Appl.，IEEE Trans. Signal Process.，Signal Process.，IEEE Signal Process. Lett.等。目前主持国自然基金面上项目一项，广东省自然基金面上项目一项。
