User Guide to Low Pass Graph Signal Processing: Graph Learning and Beyond
报告题目：User Guide to Low Pass Graph Signal Processing: Graph Learning and Beyond
报告专家： Hoi-To Wai （Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong）
报告时间：2022年11月25日，下午15：30-16：30
报告地点：ZOOM ID：819 5278 4619 (221125)
报告摘要：As a key development in the emerging field of graph signal processing, the notion of graph filters has been used to define generative models for graph data which explains many examples of network dynamics. With this interpretation, classical signal processing tools such as frequency analysis have been applied with analogous interpretation to graph data, generating new insights for data science. In this talk, we present a “user guide” on a specific yet common class of graph data, where the generating graph filters are low pass such that the filter attenuates contents in the higher graph frequencies while retaining contents in the lower graph frequencies. Our choice is motivated by the prevalence of low pass models in application domains such as social networks, financial markets, and power systems. We demonstrate how to leverage properties of low pass graph filters by focusing on its applications to graph learning. We will discuss recent results such as graph topology learning, high level graph topology features inference (such as community, centrality), etc.
专家简介：Hoi-To Wai received his B.Eng. degree and his M.Phil. degree in electronic engineering from the Chinese University of Hong Kong (CUHK) and his Ph.D. degree in electrical engineering from Arizona State University (ASU). He is an assistant professor in the Department of Systems Engineering and Engineering Management, CUHK, and previously held research positions at ASU; UC Davis; Telecom ParisTech; Ecole Polytechnique; and MIT. His research interests include graph signal processing, machine learning, and distributed optimization. His papers are mainly published in prestigious venues such as COLT, NeurIPS, AISTATS, IEEE Transactions on Signal Processing, IEEE Transactions on Signal and Information Processing over Networks, SIAM Journal of Optimization, etc. His dissertation received the Dean’s Dissertation Award from ASU, and he received a Best Student Paper Award at the IEEE ICASSP. He serves as an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks and a member of the Data Science Initiative of the IEEE Signal Processing Society.
邀请人：王治国
