Dimensionality reduction methods aimed at preserving the data topology
have shown to be suitable for reaching high-quality embedded data. In particular,
those based on divergences such as stochastic neighbour embedding (SNE).
The big advantage of SNE and its variants is that the neighbor preservation is
done by optimizing the similarities in both high- and low-dimensional space. This
work presents a brief review of SNE-based methods. Also, a comparative analysis
of the considered methods is provided, which is done on important aspects such
as algorithm implementation, relationship between methods, and performance.
The aim of this paper is to investigate recent alternatives to SNE as well as to
provide substantial results and discussion to compare them.