Graph Signal Processing for Machine Learning Applications: NEW INSIGHTS AND ALGORITHMS
Graph signal processing (GSP) is an active area of research that seeks to extend to signals defined on irregular graphs tools concepts such as frequency, filtering and sampling that are well understood for conventional signals defined on regular grids. As an example this leads to the definition of so called, graph Fourier transforms (GFTs). In this talk we will provide an introduction to basic GSP concepts developed over the last few years. Then we will investigate how GSP concepts can allow us to view machine learning problems from a different perspective. Specifically, we will discuss our recent work in i) graph representations that take into account local data structure ii) graph signal sampling interpretations of semi-supervised learning, and iii) a GSP-based analysis of deep learning systems.
Antonio Ortega received his undergraduate and doctoral degrees from Universidad Politécnica de Madrid, Madrid, Spain and Columbia University, New York, NY, respectively. In 1994 he joined the Electrical and Computer Engineering department at the University of Southern California (USC), where he is currently a Professor and has served as Associate Chair. He is a Fellow of the IEEE and EURASIP, and a member of ACM and APSIPA. He is currently a member of the Board of Governors of the IEEE Signal Processing Society. He has received several paper awards, including the 2016 Signal Processing Magazine award. His recent research work is focusing on graph signal processing, machine learning, multimedia compression and wireless sensor networks.