On the Sample Complexity of Learning under Invariance and Geometric Stability

TL;DR

This paper analyzes how leveraging invariance and geometric stability in high-dimensional learning problems can reduce sample complexity, providing theoretical rates and demonstrating benefits of invariant kernels.

Contribution

It introduces non-parametric convergence rates for invariant kernel methods and quantifies sample complexity improvements based on group size and spectral properties.

Findings

Invariant kernels reduce sample complexity proportional to group size.

Sample complexity improvements depend on spectral properties of the invariance group.

Extensions include geometric stability to small deformations beyond invariance groups.

Abstract

Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geometric priors in the problem at hand, such as invariance to translations, permutation subgroups, or stability to small deformations. We study the sample complexity of learning problems where the target function presents such invariance and stability properties, by considering spherical harmonic decompositions of such functions on the sphere. We provide non-parametric rates of convergence for kernel methods, and show improvements in sample complexity by a factor equal to the size of the group when using an invariant kernel over the group, compared to the corresponding non-invariant kernel. These improvements are valid when the sample size is large enough, with an asymptotic behavior that depends on spectral properties of the group. Finally, these gains are extended beyond invariance groups to also cover geometric stability to small deformations, modeled here as subsets (not necessarily subgroups) of permutations.