  Random projections became popular tools to process big data. In particular,
when applied to Nonnegative Matrix Factorization (NMF), it was shown that
structured random projections were far more efficient than classical strategies
based on Gaussian compression. However, they remain costly and might not fully
benefit from recent fast random projection techniques. In this paper, we thus
investigate an alternative to structured ran-om projections-named Gaussian
compression stream-which (i) is based on Gaussian compressions only, (ii) can
benefit from the above fast techniques, and (iii) is shown to be well-suited to
NMF.
