The ability to evaluate nonlinear function classes rapidly is
  crucial for nonparametric estimation. We propose an
  improvement to random kitchen sinks that offers $O(n \log d)$ computation and $O(n)$ storage for $n$ basis functions in
  $d$ dimensions without sacrificing accuracy.  

  We show how one may adjust the regularization
  properties of the kernel simply by changing the spectral
  distribution of the projection matrix. Experiments show
  that we achieve identical accuracy to full kernel expansions and
  random kitchen sinks 100x faster with 1000x less memory.