5 papers
Samplet compression for conditionally positive definite kernels and universal Kriging
Sara Avesani, Rüdiger Kempf, Michael Multerer +1
We present a samplet-based framework for the efficient numerical solution of saddle-point systems arising from conditionally positive definite (CPD) kernel approximation in general…
Data-intrinsic approximation in metric spaces
Jürgen Dölz, Michael Multerer
Analysis and processing of data is a vital part of our modern society and requires vast amounts of computational resources. To reduce the computational burden, compressing and appr…
Fast Empirical Scenarios
Michael Multerer, Paul Schneider, Rohan Sen
We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenar…
Adaptive joint distribution learning
Damir Filipovic, Michael Multerer, Paul Schneider
We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensiona…
On Quasi-Localized Dual Pairs in Reproducing Kernel Hilbert Spaces
Helmut Harbrecht, Rüdiger Kempf, Michael Multerer
In scattered data approximation, the span of a finite number of translates of a chosen radial basis function is used as approximation space and the basis of translates is used for…