@inproceedings{5179,
  title = {A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery},
  journal = {Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG 2008)},
  booktitle = {MLG 2008},
  abstract = {In recent work by (Song et al., 2007), it has been proposed
  to perform clustering by maximizing a Hilbert-Schmidt
  independence criterion with respect to a predefined
  cluster structure Y , by solving for the partition
  matrix, II. We extend this approach here to the
  case where the cluster structure Y is not fixed, but is
  a quantity to be optimized; and we use an independence
  criterion which has been shown to be more sensitive
  at small sample sizes (the Hilbert-Schmidt Normalized
  Information Criterion, or HSNIC, Fukumizu
  et al., 2008). We demonstrate the use of this framework
  in two scenarios. In the first, we adopt a cluster
  structure selection approach in which the HSNIC is
  used to select a structure from several candidates. In
  the second, we consider the case where we discover
  structure by directly optimizing Y.},
  pages = {1-3},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2008},
  slug = {5179},
  author = {Blaschko, MB. and Gretton, A.},
  month_numeric = {7}
}