Learning Taxonomies by Dependence Maximization

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-ofthe-art algorithms in the literature (both spectral clustering and a previous dependence maximization approach). We demonstrate our algorithm on image and text data.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Matthew B. Blaschko, Arthur Gretton
• Keywords: Information Technology, NIPS 2008, Numerical Taxonomy, Taxonomy, Unsupervised Algorithms