  We present a new paradigm for speeding up randomized computations of several
frequently used functions in machine learning. In particular, our paradigm can
be applied for improving computations of kernels based on random embeddings.
Above that, the presented framework covers multivariate randomized functions.
As a byproduct, we propose an algorithmic approach that also leads to a
significant reduction of space complexity. Our method is based on careful
recycling of Gaussian vectors into structured matrices that share properties of
fully random matrices. The quality of the proposed structured approach follows
from combinatorial properties of the graphs encoding correlations between rows
of these structured matrices. Our framework covers as special cases already
known structured approaches such as the Fast Johnson-Lindenstrauss Transform,
but is much more general since it can be applied also to highly nonlinear
embeddings. We provide strong concentration results showing the quality of the
presented paradigm.
