I'm relatively new to t-SNE and it seems that unlike in other dimensionality-reduction techniques, the dimensions in t-SNE are hard to interpret. The contribution of the variables can easily be accessed in PCA and MCA, and one can regress on the dimensions of MDS to know how each variable affects the position of the points in the n-dimensional space. Since there is no linear relationship between the dimensions and the points in t-SNE (at least that's what I've read somewhere), isn't there a way to get to know how the variables explain the dimensions?
I motivate my choice of t-SNE because it takes much less time to represent the distances than e.g. MDS. I'm using R version 3.4.4 (64-bit) and the
Rtsne function from the package of the same name on a packard bell (Intel(R) Celeron(R) CPU B830 @ 1.80 GHz with 8,00 G RAM).
Thanking you in advance.