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arXiv · 1507.08379

VMF-SNE: Embedding for Spherical Data

Mian Wang·Dong Wang
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Abstract

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.

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Mian Wang, Dong Wang. 2015-07-30. VMF-SNE: Embedding for Spherical Data. https://doi.org/10.1007/s11040-015-9171-z

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