mlsanityData to 7 Oct 2026

Directional Statistics in Machine Learning: a Brief Review

arXiv 1605.00316 ↗First posted 1 May 2016stat.MLcited by 13

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Abstract (arXiv)

The modern data analyst must cope with data encoded in various forms, vectors, matrices, strings, graphs, or more. Consequently, statistical and machine learning models tailored to different data encodings are important. We focus on data encoded as normalized vectors, so that their "direction" is more important than their magnitude. Specifically, we consider high-dimensional vectors that lie either on the surface of the unit hypersphere or on the real projective plane. For such data, we briefly review common mathematical models prevalent in machine learning, while also outlining some technical aspects, software, applications, and open mathematical challenges.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2016.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 3; 2025: 3.

13 papers.

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  1. 2022Spherical Sliced-Wassersteincited by 31
  2. 2021Learning Riemannian Manifolds for Geodesic Motion Skillscited by 23
  3. 2023Markovian Sliced Wasserstein Distances: Beyond Independent Projectionscited by 11
  4. 2023Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstructioncited by 7
  5. 2022Reactive Motion Generation on Learned Riemannian Manifoldscited by 6
  6. 2024ComFe: An Interpretable Head for Vision Transformerscited by 2
  7. 2024Dynamic Feature Learning and Matching for Class-Incremental Learningcited by 1
  8. 2024Kuramoto Oscillators and Swarms on Manifolds for Geometry Informed Machine Learningcited by 1
  9. 2021Von Mises-Fisher Elliptical Distributioncited by 0
  10. 2022Weak Supervision in Analysis of News: Application to Economic Policy Uncertaintycited by 0
  11. 2025Precise Bayesian Neural Networkscited by 0
  12. 2025Relation-Aware Slicing in Cross-Domain Alignmentcited by 0
1 more
  1. 2025Angular Regularization for Positive-Unlabeled Learning on the Hyperspherecited by 0