Is it possible to use scikit TSNE on a large sparse matrix?

Question

The scikit documentation explains fit_transform can only be used for dense matrices, but I have a sparse matrix in csr format which I want to perform tsne on. The documentation says to use the fit method for sparse matrices, but this doesn't return the low dimensional embedding.

I appreciate I could use the .todense() method as in this question, but my data set is very large (0.4*10^6 rows and 0.5*10^4 columns) so wont fit in memory. Really, it would be nice to do this using sparse matrices. Is there a way to use scikit TSNE (or any other python implementation of TSNE) to reduce the dimensionality of a large sparse matrix and return the low dimensional embedding to then visualize?

Answer 1

From that same documentation:

It is highly recommended to use another dimensionality reduction method (e.g. PCA for dense data or TruncatedSVD for sparse data) to reduce the number of dimensions to a reasonable amount (e.g. 50) if the number of features is very high. This will suppress some noise and speed up the computation of pairwise distances between samples.

Use sklearn.decomposition.TruncatedSVD instead.