An overview of uniformity tests on the hypersphere
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Abstract (arXiv)
When modeling directional data, that is, unit-norm multivariate vectors, a first natural question is to ask whether the directions are uniformly distributed or, on the contrary, whether there exist modes of variation significantly different from uniformity. We review in this article a reasonably exhaustive collection of uniformity tests for assessing uniformity in the hypersphere. Specifically, we review the classical circular-specific tests, the large class of Sobolev tests with its many notable particular cases, some recent alternative tests, and novel results in the high-dimensional low-sample size case. A reasonably comprehensive bibliography on the topic is provided.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2023Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gapcited by 14
- 2022Self-supervised learning with rotation-invariant kernelscited by 2
- 2026Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifoldscited by 0
- 2026Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Misscited by 0
- 2026Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss7 Oct 2026
- 2026Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds7 Jul 2026
- 2023Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap11 Mar 2023
- 2022Self-supervised learning with rotation-invariant kernels28 Jul 2022