Publication | Open Access
Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels
160
Citations
17
References
2008
Year
Heat KernelEngineeringResolvent KernelGeometric Partial Differential EquationManifold LearningRiemannian GeometryManifold ModelingGlobal AnalysisComputer ScienceManifold ParametrizationsLocal CoordinatesFunctional AnalysisEuclidean DomainsRiemannian Manifold
We use heat kernels or eigenfunctions of the Laplacian to construct local coordinates on large classes of Euclidean domains and Riemannian manifolds (not necessarily smooth, e.g., with (alpha) metric). These coordinates are bi-Lipschitz on large neighborhoods of the domain or manifold, with constants controlling the distortion and the size of the neighborhoods that depend only on natural geometric properties of the domain or manifold. The proof of these results relies on novel estimates, from above and below, for the heat kernel and its gradient, as well as for the eigenfunctions of the Laplacian and their gradient, that hold in the non-smooth category, and are stable with respect to perturbations within this category. Finally, these coordinate systems are intrinsic and efficiently computable, and are of value in applications.
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