Name
Abstract | ||
|---|---|---|
Any closed, connected Riemannian manifold M can be smoothly embedded by its Laplacian eigenfunction maps into Rm for some m. We call the smallest such m the maximal embedding dimension of M. We show that the maximal embedding dimension of M is bounded from above by a constant depending only on the dimension of M, a lower bound for injectivity radius, a lower bound for Ricci curvature, and a volume bound. We interpret this result for the case of surfaces isometrically immersed in R3, showing that the maximal embedding dimension only depends on bounds for the Gaussian curvature, mean curvature, and surface area. Furthermore, we consider the relevance of these results for shape registration. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.acha.2014.03.002 | Applied and Computational Harmonic Analysis |
Keywords | Field | DocType |
Spectral embedding,Eigenfunction embedding,Eigenmap,Diffusion map,Global point signature,Heat kernel embedding,Shape registration,Nonlinear dimensionality reduction,Manifold learning | Eigenfunction,Embedding,Ricci curvature,Mathematical analysis,Upper and lower bounds,Riemannian manifold,Mean curvature,Mathematics,Gaussian curvature,Laplace operator | Journal |
Volume | Issue | ISSN |
37 | 3 | 1063-5203 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Bates | 1 | 17 | 2.49 |