Description
The current implementation of t-SNE uses the exact method which computes the full pairwise high-dimensional affinity matrix as well as the exact forces applied to the low-dimensional points. Since their original work, the authors of t-SNE have released a subsequent paper detailing the use of spatial trees for efficient nearest neighbor search as well as for approximating the forces of distant bodies of points in low dimensions. Since Rubix ML already employs spatial trees to accelerate other learners and given that the O(n^2) time complexity of computing affinities is intractable for large datasets, I recommend implementing the Barnes-Hut approximation of t-SNE.
Note that t-SNE would require a spatial tree as a dependency instead of a distance kernel which would be a very minor BC break.