Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures

摘要

This paper surveys recent progress in the in- vestigation of the underlying discrete proximity structures of ge- ometric clustering with respect to the divergence in information geometry. Geometric clustering with respect to the divergence provides powerful unsupervised learning algorithms, and can be applied to classifying and obtaining generalizations of complex objects represented in the feature space. The proximity relation, defined by the Voronoi diagram by the divergence, plays an im- portant role in the design and analysis of such algorithms.