@inproceedings{2133,
  title = {The Kernel Mutual Information},
  journal = {IEEE ICASSP Vol. 4},
  abstract = {We introduce a new contrast function, the kernel mutual information
  (KMI), to measure the degree of independence of continuous random
  variables. This contrast function provides an approximate upper bound
  on the mutual information, as measured near independence, and is based
  on a kernel density estimate of the mutual information between a discretised
  approximation of the continuous random variables. We show that Bach
  and Jordan‘s kernel generalised variance (KGV) is also an upper bound
  on the same kernel density estimate, but is looser. Finally, we suggest
  that the addition of a regularising term in the KGV causes it to approach
  the KMI, which motivates the introduction of this regularisation.},
  pages = {880-883},
  organization = {Max-Planck-Gesellschaft},
  institution = {MPI for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2003},
  author = {Gretton, A. and Herbrich, R. and Smola, A.},
  month_numeric = {4}
}