Pages that link to "Item:Q3010071"

The following pages link to Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels (Q3010071):

Displaying 38 items.

• A filtering technique for Markov chains with applications to spectral embedding (Q262948) (← links)
• Embedding Riemannian manifolds by the heat kernel of the connection Laplacian (Q329518) (← links)
• A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching (Q408814) (← links)
• Multi-scale geometric methods for data sets. II: Geometric multi-resolution analysis (Q413654) (← links)
• Geometry of the ergodic quotient reveals coherent structures in flows (Q449067) (← links)
• Compressive wave computation (Q544804) (← links)
• The embedding dimension of Laplacian eigenfunction maps (Q741268) (← links)
• Natural graph wavelet packet dictionaries (Q829903) (← links)
• A unified framework for harmonic analysis of functions on directed graphs and changing data (Q1742819) (← links)
• Parsimonious representation of nonlinear dynamical systems through manifold learning: a chemotaxis case study (Q1742825) (← links)
• Some extensions of E. Stein's work on Littlewood-Paley theory applied to symmetric diffusion semigroups (Q2050522) (← links)
• Data-driven efficient solvers for Langevin dynamics on manifold in high dimensions (Q2105115) (← links)
• Approximation of functions over manifolds: a moving least-squares approach (Q2199793) (← links)
• Quadrature points via heat kernel repulsion (Q2285747) (← links)
• Nonparametric analysis of non-Euclidean data on shapes and images (Q2316991) (← links)
• Data-driven spectral decomposition and forecasting of ergodic dynamical systems (Q2325539) (← links)
• Intrinsic modeling of stochastic dynamical systems using empirical geometry (Q2347900) (← links)
• Multiscale geometric methods for data sets. I: Multiscale SVD, noise and curvature. (Q2402490) (← links)
• Spectral echolocation via the wave embedding (Q2402493) (← links)
• An equivalence between the limit smoothness and the rate of convergence for a general contraction operator family (Q2657241) (← links)
• Manifold embeddings by heat kernels of connection Laplacian (Q2691627) (← links)
• Embeddings of Riemannian manifolds with heat kernels and eigenfunctions (Q2790837) (← links)
• EMBEDDING RIEMANNIAN MANIFOLDS VIA THEIR EIGENFUNCTIONS AND THEIR HEAT KERNEL (Q3166794) (← links)
• Data-Driven Reduction for a Class of Multiscale Fast-Slow Stochastic Dynamical Systems (Q3188143) (← links)
• Differential Geometry for Model Independent Analysis of Images and Other Non-Euclidean Data: Recent Developments (Q3296391) (← links)
• Explore Intrinsic Geometry of Sleep Dynamics and Predict Sleep Stage by Unsupervised Learning Techniques (Q3384135) (← links)
• Universal local parametrizations via heat kernels and eigenfunctions of the Laplacian (Q3560524) (← links)
• Approximate Quadrature Measures on Data-Defined Spaces (Q4611834) (← links)
• Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability (Q4907484) (← links)
• Gaussian Process Landmarking on Manifolds (Q5025781) (← links)
• (Q5214254) (← links)
• Dynamics-Adapted Cone Kernels (Q5249799) (← links)
• Multiscale Nonrigid Point Cloud Registration Using Rotation-Invariant Sliced-Wasserstein Distance via Laplace--Beltrami Eigenmap (Q5266381) (← links)
• Solving Partial Differential Equations on Manifolds From Incomplete Interpoint Distance (Q5364201) (← links)
• Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning (Q6171687) (← links)
• Spatiotemporal analysis using Riemannian composition of diffusion operators (Q6185681) (← links)
• Applications of No-Collision Transportation Maps in Manifold Learning (Q6202282) (← links)
• Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics (Q6567586) (← links)