Explore graph adjacency spectral embeddings through this 1-hour 13-minute webinar presented by Gonzalo Mateos from the University of Rochester. Delve into algorithmic advances and applications as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Gain insights into this specialized topic within graph theory and data science, learning about cutting-edge techniques and their practical implementations. Enhance your understanding of spectral graph theory and its relevance to modern data analysis and signal processing applications.
Graph Adjacency Spectral Embeddings - Algorithmic Advances and Applications
IEEE Signal Processing Society via YouTube
Overview
Syllabus
Graph adjacency spectral embeddings: Algorithmic advances and applications
Taught by
IEEE Signal Processing Society
Related Courses
-
Vertex, Edge, Clique - Understanding Graph Theory Fundamentals
-
Signal Processing on Graphs and Complexes
-
Low Distortion Embeddings With Bottom-up Manifold Learning
-
Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks
-
Connecting the Dots - Leveraging GSP to Learn Graphs From Nodal Observations
-
Learning Distances for Attributed Graphs with Optimal Transport
