@techreport{5618,
  title = {Taxonomy Inference Using Kernel Dependence
  Measures},
  abstract = {We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously
  cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms
  work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a
  more informative visualization of complex data than simple clustering; in addition, taking into account the relations
  between different clusters is shown to substantially improve the quality of the clustering, when compared
  with state-of-the-art algorithms in the literature (both spectral clustering and a previous dependence maximization
  approach). We demonstrate our algorithm on image and text data.},
  number = {181},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2008},
  slug = {5618},
  author = {Blaschko, MB. and Gretton, A.},
  month_numeric = {11}
}