Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures
Summary :
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.
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- IEICE TRANSACTIONS on Information Vol.E83-D No.1 pp.27-35
- Publication Date
- 2000/01/25
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- Special Section INVITED PAPER (Special Issue on Surveys on Discovery Science)
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Hiroshi IMAI, Mary INABA, "Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 1, pp. 27-35, January 2000, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_1_27/_p
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@ARTICLE{e83-d_1_27,
author={Hiroshi IMAI, Mary INABA, },
journal={IEICE TRANSACTIONS on Information},
title={Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures},
year={2000},
volume={E83-D},
number={1},
pages={27-35},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures
T2 - IEICE TRANSACTIONS on Information
SP - 27
EP - 35
AU - Hiroshi IMAI
AU - Mary INABA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2000
AB - 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.
ER -
English
