For random geometric hypergraphs, the standard variational semi-supervised learning problem converges in the large-data limit to a density-weighted p-Laplacian problem, making it a first-order graph method; the proposed HOHL scheme converges to a higher-order Sobolev-type seminorm.
Analysis ofp-Laplacian regularization in semisupervised learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Analysis of Semi-Supervised Learning on Hypergraphs
For random geometric hypergraphs, the standard variational semi-supervised learning problem converges in the large-data limit to a density-weighted p-Laplacian problem, making it a first-order graph method; the proposed HOHL scheme converges to a higher-order Sobolev-type seminorm.