Strong uniform consistency with rates for kernel density estimators with general kernels on manifolds

Journal Article (Journal Article)

When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric. To handle this emerging challenge, we provide a strong uniform consistency result with the $L^\infty $ convergence rate for KDE on Riemannian manifolds with Riemann integrable kernels (in the ambient Euclidean space). We also provide an $L^1$ consistency result for kernel density estimation on Riemannian manifolds with Lebesgue integrable kernels. The isotropic kernels considered in this paper are different from the kernels in the Vapnik-Chervonenkis class that are frequently considered in statistics society. We illustrate the difference when we apply them to estimate the probability density function. Moreover, we elaborate the delicate difference when the kernel is designed on the intrinsic manifold and on the ambient Euclidian space, both might be encountered in practice. At last, we prove the necessary and sufficient condition for an isotropic kernel to be Riemann integrable on a submanifold in the Euclidean space.

Full Text

Duke Authors

Cited Authors

  • Wu, HT; Wu, N

Published Date

  • June 1, 2022

Published In

Volume / Issue

  • 11 / 2

Start / End Page

  • 781 - 799

Electronic International Standard Serial Number (EISSN)

  • 2049-8772

Digital Object Identifier (DOI)

  • 10.1093/imaiai/iaab014

Citation Source

  • Scopus