In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data. We propose a novel method to learn a graph with smaller edge weight upper bounds compared to combinatorial Laplacian approaches. Experimentally, our approach yields much sparser graphs compared to a combinatorial Laplacian approach, with a more interpretable model.
@article{arxiv.2303.08552,
title = {Joint Graph and Vertex Importance Learning},
author = {Benjamin Girault and Eduardo Pavez and Antonio Ortega},
journal= {arXiv preprint arXiv:2303.08552},
year = {2023}
}
Comments
submitted to 2023 31st European Signal Processing Conference (EUSIPCO)