Graph Constructions for Machine Learning Applications: New Insights and Algorithms
Dr. Antonio Ortega
- Department of Electrical and Computer Engineering
University of Southern California
Graphs have long been used in various problems, such as analyzing social networks, machine learning, network protocol optimization, or image processing. In the last few years, a growing body of work has been developed to extend and complement well-known concepts in spectral graph theory, leading to the emergence of Graph Signal Processing (GSP) as a broad research field. In this talk, we summarize recent results that lead to a GSP perspective of machine learning problems. The main observation is that representations of sample data points (e.g., images in a training set) can be used to construct graphs, with nodes representing samples, label information resulting in graph signals, and edge weights capturing the relative positions of samples in feature space. We will first review how this perspective has been used in well-known techniques for label propagation and semi-supervised learning. Then, we will introduce the non-negative kernel regression (NNK) graph construction, describe its properties, and introduce example applications in machine learning areas such as i) model explainability, ii) local interpolative classification, and iii) self-supervised learning.
Antonio Ortega received his undergraduate and doctoral degrees from the Universidad Politecnica de Madrid, Madrid, Spain and Columbia University, New York, NY, respectively. At Columbia, he was supported by a Fulbright Scholarship. 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. He was the Editor-in-Chief of the IEEE Transactions of Signal and Information Processing over Networks and will serve as VP of Publications for the IEEE Signal Processing Society from January 2024. He has received several paper awards, including the 2016 Signal Processing Magazine award. His recent research focuses on graph signal processing, machine learning, and multimedia compression. Over 40 PhD students have completed their PhD thesis under his supervision, and his work has led to over 400 publications in international conferences and journals and several patents. He is the author of the book, "Introduction to Graph Signal Processing," published by Cambridge University Press in 2022.