  Feature ranking and selection is a widely used approach in various
applications of supervised dimensionality reduction in discriminative machine
learning. Nevertheless there exists significant evidence on feature ranking and
selection algorithms based on any criterion leading to potentially sub-optimal
solutions for class separability. In that regard, we introduce emerging
information theoretic feature transformation protocols as an end-to-end neural
network training approach. We present a dimensionality reduction network
(MMINet) training procedure based on the stochastic estimate of the mutual
information gradient. The network projects high-dimensional features onto an
output feature space where lower dimensional representations of features carry
maximum mutual information with their associated class labels. Furthermore, we
formulate the training objective to be estimated non-parametrically with no
distributional assumptions. We experimentally evaluate our method with
applications to high-dimensional biological data sets, and relate it to
conventional feature selection algorithms to form a special case of our
approach.
