Non-parametric manifold learning
From MaRDI portal
Publication:6635576
DOI10.1214/24-ejs2291MaRDI QIDQ6635576
Publication date: 12 November 2024
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
consistencyLaplace-Beltrami operatorWasserstein distancemanifold learninggraph LaplacianConnes' distance formula
Cites Work
- Unnamed Item
- Unnamed Item
- Manifold estimation and singular deconvolution under Hausdorff loss
- Semi-supervised learning on Riemannian manifolds
- Möbius deconvolution on the hyperbolic plane with application to impedance density estimation
- Spectral convergence of graph Laplacian and heat kernel reconstruction in \(L^\infty\) from random samples
- Spectral truncations in noncommutative geometry and operator systems
- Triangles and triple products of Laplace eigenfunctions
- Unconstrained and curvature-constrained shortest-path distances and their approximation
- The spectral function of an elliptic operator
- Gradient estimate of an eigenfunction on a compact Riemannian manifold without boundary
- $C^\infty$ approximations of convex, subharmonic, and plurisubharmonic functions
- Empirical graph Laplacian approximation of Laplace–Beltrami operators: Large sample results
- Some Statistical Approaches to Multidimensional Scaling Data
- Spectral Exterior Calculus
- Latent Surface Models for Networks Using Aggregated Relational Data
- Local Linear Regression on Manifolds and Its Geometric Interpretation
- Multiscale Representations for Manifold-Valued Data
- Compact metric spaces, Fredholm modules, and hyperfiniteness
This page was built for publication: Non-parametric manifold learning