Department of Information Science, University of Tokyo
抄録
This paper surveys recent progress in the investigation of the underlying discrete proximity structures of geometric 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 important role in the design and analysis of such algorithms.
内容記述
INVITED PAPER (Special Issue on Surveys on Discovery Science)
Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures
Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures 2000.pdf
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