Taxonomy Inference Using Kernel Dependence Measures

2008

Technical Report

ei

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.

@techreport{5618,
  title = {Taxonomy Inference Using Kernel Dependence
  Measures},
  author = {Blaschko, MB. and Gretton, A.},
  number = {181},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2008},
  doi = {},
  month_numeric = {11}
}