# VMF-SNE: Embedding for Spherical Data

arXiv: https://arxiv.org/abs/1507.08379 · HTML: https://mlsanity.com/paper/1507.08379/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2015.

## Built on by (most cited first, 2 of 2)

- [Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings](https://mlsanity.com/paper/2303.09352/) (2023)
- [A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models](https://mlsanity.com/paper/2311.17093/) (2023)

## Abstract (arXiv)

T-SNE is a well-known approach to embedding high-dimensional data and has been widely used in data visualization. The basic assumption of t-SNE is that the data are non-constrained in the Euclidean space and the local proximity can be modelled by Gaussian distributions. This assumption does not hold for a wide range of data types in practical applications, for instance spherical data for which the local proximity is better modelled by the von Mises-Fisher (vMF) distribution instead of the Gaussian. This paper presents a vMF-SNE embedding algorithm to embed spherical data. An iterative process is derived to produce an efficient embedding. The results on a simulation data set demonstrated that vMF-SNE produces better embeddings than t-SNE for spherical data.