  The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a
measure of data similarity and was demonstrated effective in machine learning
tasks. In order to use the GMM kernel for large-scale datasets, the prior work
resorted to the (generalized) consistent weighted sampling (GCWS) to convert
the GMM kernel to linear kernel. We call this approach as ``GMM-GCWS''.
  In the machine learning literature, there is a popular algorithm which we
call ``RBF-RFF''. That is, one can use the ``random Fourier features'' (RFF) to
convert the ``radial basis function'' (RBF) kernel to linear kernel. It was
empirically shown in (Li, 2016) that RBF-RFF typically requires substantially
more samples than GMM-GCWS in order to achieve comparable accuracies.
  The Nystrom method is a general tool for computing nonlinear kernels, which
again converts nonlinear kernels into linear kernels. We apply the Nystrom
method for approximating the GMM kernel, a strategy which we name as
``GMM-NYS''. In this study, our extensive experiments on a set of fairly large
datasets confirm that GMM-NYS is also a strong competitor of RBF-RFF.
