Radial basis approximation of tensor fields on manifolds: From operator estimation to manifold learning

In this paper, we study the Radial Basis Function (RBF) approximation to differential operators on smooth tensor fields defined on closed Riemannian submanifolds of Euclidean space, identified by randomly sampled point cloud data. Theoretically, we establish the spectral convergence for the classical pointwise RBF discrete non-symmetric approximation of Laplacians. Numerically, we found that this formulation produces a very accurate estimation of leading spectra with large enough data. When the manifolds are unknown, the error bound of the pointwise operator estimation depends on the accuracy of the approximate local tangent spaces. To improve this approximation accuracy, we develop a second-order local SVD technique for estimating local tangent spaces on the manifold. For robust manifold learning, we introduce a symmetric RBF discrete approximation of the Laplacians induced by a weak formulation on appropriate Hilbert spaces. Unlike the non-symmetric approximation, this formulation guarantees non-negative real-valued spectra and the orthogonality of the eigenvectors. Theoretically, we establish the convergence of the eigenpairs of both the Laplace-Beltrami operator and Bochner Laplacian in the limit of large data with convergence rates. Numerically, we provide supporting examples for approximations of the Laplace-Beltrami operator and various vector Laplacians, including the Bochner, Hodge, and Lichnerowicz Laplacians.