2 papers
math.FA2025
Distributional Convergence of the Empirical Laplacians with Integral Kernels on Domains with Boundaries
Bernard Akwei, Luke Rogers, Alexander Teplyaev
Motivated by the problem of understanding theoretical bounds for the performance of the Belkin-Niyogi Laplacian eigencoordinate approach to dimension reduction in machine learning…
math.PR2024
Convergence, optimization and stability of singular eigenmaps
Bernard Akwei, Bobita Atkins, Rachel Bailey +9
Eigenmaps are important in analysis, geometry, and machine learning, especially in nonlinear dimension reduction. Approximation of the eigenmaps of a Laplace operator depends cruci…