@techreport{2212,
  title = {The Kernel Mutual Information},
  abstract = {We introduce two new functions, the kernel covariance (KC) and the kernel
   mutual information (KMI), to measure the degree of independence of several
   continuous random variables.
   The former is guaranteed to be zero if and only if the random variables
   are pairwise independent; the latter shares this property, and is in addition
   an approximate upper bound on the mutual information, as measured near
   independence, and is based on a kernel density estimate.
   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.
   The performance of the KC and KMI is verified in the context of instantaneous
   independent component analysis (ICA), by recovering both artificial and
   real (musical) signals following linear mixing.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2003},
  slug = {2212},
  author = {Gretton, A. and Herbrich, R. and Smola, AJ.},
  month_numeric = {4}
}