An efficient algorithm reconstructs manifold geometry from random geometric graphs under the manifold assumption with distance-dependent connection probabilities.
Reconstruction and interpolation of manifolds II : I nverse problems for R iemannian manifolds with partial distance data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A review of manifold fitting that distinguishes it from embedding and denoising, covers its evolution from early nonparametric methods through mathematical insights to modern statistical approaches, and highlights applications in neural networks and bioinformatics.
citing papers explorer
-
Reconstructing the Geometry of Random Geometric Graphs
An efficient algorithm reconstructs manifold geometry from random geometric graphs under the manifold assumption with distance-dependent connection probabilities.
-
Manifold Fitting: A Review of Methods and Applications
A review of manifold fitting that distinguishes it from embedding and denoising, covers its evolution from early nonparametric methods through mathematical insights to modern statistical approaches, and highlights applications in neural networks and bioinformatics.