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 year. 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 three areas:

i) novel GFT designs that can be optimized for different tasks, such as clustering or spatial data processing,
ii) a sampling interpretation of semi-supervised learning, and
iii) a GSP-based analysis of deep learning systems. 

Bio: Antonio Ortega received his undergraduate and doctoral degrees from the Universidad Politecnica de Madrid, Madrid, Spain and Columbia University, New York, NY, respectively. In 1994 he joined the Electrical 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.