Weighted nonlocal operators and their applications in semi-supervised learning

Fuente: arXiv
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Autori principali: Du, Qiang, Scott, James M.
Natura: Preprint
Pubblicazione: 2024
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author Du, Qiang
Scott, James M.
author_facet Du, Qiang
Scott, James M.
contents Motivated by problems in machine learning, we study a class of variational problems characterized by nonlocal operators. These operators are characterized by power-type weights, which are singular at a portion of the boundary. We identify a range of exponents on these weights for which the variational Dirichlet problem is well-posed. This range is determined by the ambient dimension of the problem, the growth rate of the nonlocal functional, and the dimension of the boundary portion on which the Dirichlet data is prescribed. We show the variational convergence of solutions to solutions of local weighted Sobolev functionals in the event of vanishing nonlocality.
format Preprint
id arxiv_https___arxiv_org_abs_2412_16109
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Weighted nonlocal operators and their applications in semi-supervised learning
Du, Qiang
Scott, James M.
Analysis of PDEs
Motivated by problems in machine learning, we study a class of variational problems characterized by nonlocal operators. These operators are characterized by power-type weights, which are singular at a portion of the boundary. We identify a range of exponents on these weights for which the variational Dirichlet problem is well-posed. This range is determined by the ambient dimension of the problem, the growth rate of the nonlocal functional, and the dimension of the boundary portion on which the Dirichlet data is prescribed. We show the variational convergence of solutions to solutions of local weighted Sobolev functionals in the event of vanishing nonlocality.
title Weighted nonlocal operators and their applications in semi-supervised learning
topic Analysis of PDEs
url https://arxiv.org/abs/2412.16109