On the smallest enclosing information disk

Proceedings of the 18th Annual Canadian Conference on Computational Geometry, CCCG 2006

Volumn , Issue , 2006, Pages 131-134

  Nielsen, Frank ^a   Nock, Richard ^b  

^a SONY COMPUTER SCIENCE LABORATORIES INC   (Japan)

^b University of Antilles and Guyane   (France)

Author keywords

[No Author keywords available]

Indexed keywords

NORMAL DISTRIBUTION;

BREGMAN DIVERGENCES; DISTORTION MEASURES; EUCLIDEAN ALGORITHMS; EUCLIDEAN DISTANCE; GEOMETRIC SPACE; MAXIMUM DISTANCE; SIMILARITY MEASURE; STATISTICAL MODELING;

COMPUTATIONAL GEOMETRY;

EID: 70350535769     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. S.-I. Amari and H. Nagaoka. Methods of Information Geometry, Vol. 191, AMS, 2000.
2. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with Bregman divergences. Journal of Machine Learning Research, 6:1705–1749, 2005.
3. J. Matoušek, M. Sharir, and E. Welzl. A subexponential bound for linear programming. 16(4–5):498–516, 1996.
4. F. Nielsen. Visual Computing: Geometry, Graphics, and Vision. Charles River Media, ISBN 1584504277, 2005.
5. F. Nielsen and R. Nock. Approximating smallest enclosing balls. In ICCSA CGA, LNCS #3045. Springer, 2004.
6. R. Nock and F. Nielsen. Fitting the smallest enclosing Bregman ball. In ECML, LNCS #3720, 2005.
7. E. Welzl. Smallest enclosing disks (balls and ellipsoids). Lecture Notes Comput. Sci., 555:359–370, 1991.