  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.
