Entropy and sampling numbers of classes of ridge functions
Sebastian Mayer, Tino Ullrich, Jan Vybiral

TL;DR
This paper investigates the approximation and sampling complexities of high-dimensional ridge function classes, revealing their entropy properties and the impact of additional smoothness assumptions on sampling difficulty.
Contribution
It provides a detailed analysis of entropy and sampling numbers for ridge functions, highlighting the contrast between entropy compactness and sampling complexity, and introduces new tractability insights.
Findings
Entropy numbers show ridge classes are as compact as univariate Lipschitz functions.
Sampling ridge functions on the Euclidean ball faces the curse of dimensionality.
Additional smoothness assumptions can drastically reduce sampling complexity.
Abstract
We study properties of ridge functions in high dimensions from the viewpoint of approximation theory. The considered function classes consist of ridge functions such that the profile is a member of a univariate Lipschitz class with smoothness (including infinite smoothness), and the ridge direction has -norm . First, we investigate entropy numbers in order to quantify the compactness of these ridge function classes in . We show that they are essentially as compact as the class of univariate Lipschitz functions. Second, we examine sampling numbers and face two extreme cases. In case , sampling ridge functions on the Euclidean unit ball faces the curse of dimensionality. It is thus as difficult as sampling general multivariate Lipschitz functions, a result in sharp contrast to the result on entropy numbers.…
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Taxonomy
TopicsMathematical Approximation and Integration · Advanced Numerical Analysis Techniques · Mathematical functions and polynomials
