arXiv stat.ML
· Papers
On the convergence of graph Laplacians with a symmetric divergence
arXiv:2607.05892v1 Announce Type: new Abstract: When analyzing a manifold learning algorithm for data lying on a smooth, compact, connected Riemannian submanifold $(mathcal{M}, g)$ of $mathbb{R}^d$, a key estimate for the geodesic distance $d_g$ is that there exists $K > 0$ such that $0 leq d_g(p, q)^2 - |p-q|^2