We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and define local curvature based on the laziness of a random walk starting at a point or region of the data. We show that this laziness directly relates to volume comparison results from Riemannian geometry. We then extend this scalar curvature notion to an entire quadratic form using neural network estimations based on the diffusion map of point-cloud data. We show applications of both estimations on toy data, single-cell data and on estimating local Hessian matrices of neural network loss landscapes.
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Diffusion, density, and defects on spheres
Simulations of colloids on spherical surfaces show that self-diffusion, local density, and topological defects are curvature-independent until freezing, after which topological charge distribution mediates curvature-dependent diffusion.
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- Award ID(s):
- 2224413
- PAR ID:
- 10596294
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 20
- Issue:
- 32
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 6371 to 6383
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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