It is customary to represent the output of these operations directly in a dense lowdimensional space.
the asymmetric similarity measures we use assume sparse vectors (or the "inclusion" criterion would be meaningless), so we project back the outcome of SVD and NMF to sparse 10K-dimensional but low-rank spaces.