Kernel two-sample tests for manifold data
From MaRDI portal
Publication:6589563
DOI10.3150/23-bej1685MaRDI QIDQ6589563
Publication date: 20 August 2024
Published in: Bernoulli (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sequential multi-sensor change-point detection
- Intrinsic dimension identification via graph-theoretic methods
- Inference for functional data with applications
- On the bootstrap of \(U\) and \(V\) statistics
- Tests of goodness of fit based on the \(L_2\)-Wasserstein distance
- Change detection via affine and quadratic detectors
- On the empirical estimation of integral probability metrics
- Spectral convergence of graph Laplacian and heat kernel reconstruction in \(L^\infty\) from random samples
- Improved spectral convergence rates for graph Laplacians on \(\varepsilon \)-graphs and \(k\)-NN graphs
- Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
- Error estimates for spectral convergence of the graph Laplacian on random geometric graphs toward the Laplace-Beltrami operator
- Asymptotic distribution and detection thresholds for two-sample tests based on geometric graphs
- Diffusion maps
- From graph to manifold Laplacian: the convergence rate
- An Intrinsic Dimensionality Estimator from Near-Neighbor Information
- Studies in the history of probability and statistics XLIV. A forerunner of the t-distribution
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Goodness-of-Fit Tests on Manifolds
- Two-sample statistics based on anisotropic kernels
- Convergence of graph Laplacian with kNN self-tuned kernels
- Probability Inequalities for Sums of Bounded Random Variables
- Learning Theory
- A Review of Image Denoising Algorithms, with a New One
- The Kolmogorov-Smirnov Test for Goodness of Fit
- The generalization of Student's ratio.
This page was built for publication: Kernel two-sample tests for manifold data