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Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
[article]
2020
arXiv
pre-print
The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an increasingly important role in the success of many graph-based representations and algorithms in the field of machine learning and graph signal processing. In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that
arXiv:2008.10065v1
fatcat:vpyt4einyfewtam6xx5rl7676a