AbstractIn this paper we study $$L_2$$
L
2
-norm sampling discretization and sampling recovery of complex-valued functions in RKHS on $$D \subset \mathbb {R}^d$$
D
⊂
R
d
based on random function samples. We only assume the finite trace of the kernel (Hilbert–Schmidt embedding into $$L_2$$
L
2
) and provide several concrete estimates with precise constants for the corresponding worst-case errors. In general, our analysis does not need any additional assumptions and also includes the case of non-Mercer kernels and also non-separable RKHS. The fail probability is controlled and decays polynomially in n, the number of samples. Under the mild additional assumption of separability we observe improved rates of convergence related to the decay of the singular values. Our main tool is a spectral norm concentration inequality for infinite complex random matrices with independent rows complementing earlier results by Rudelson, Mendelson, Pajor, Oliveira and Rauhut.
A Concentration Inequality for Random Polytopes, Dirichlet–Voronoi Tiling Numbers and the Geometric Balls and Bins Problem
