Hypothesis testing, information divergence and computational geometry

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8085 LNCS, Issue , 2013, Pages 241-248

  Nielsen, Frank ^a  

^a SONY COMPUTER SCIENCE LABORATORIES INC   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION DIVERGENCE; MAXIMUM A POSTERIORI PROBABILITIES; MULTIPLE HYPOTHESIS TESTING; NEAREST NEIGHBOR CLASSIFIER; NEAREST NEIGHBOR QUERIES; OPTIMAL DECISION-RULE; PROBABILITY OF ERRORS; STATISTICAL DISTANCE;

INFORMATION SCIENCE; PROBABILITY DISTRIBUTIONS; STATISTICAL TESTS;

COMPUTATIONAL GEOMETRY;

EID: 84884919229     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40020-9_25     Document Type: Conference Paper

References (15)

1. Leang, C.C., Johnson, D.H.: On the asymptotics of M-hypothesis Bayesian detection. IEEE Transactions on Information Theory 43(1), 280-282 (1997)
2. Hellman, M.E., Raviv, J.: Probability of error, equivocation and the Chernoff bound. IEEE Transactions on Information Theory 16, 368-372 (1970)
3. Chernoff, H.: A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Annals of Mathematical Statistics 23, 493-507 (1952)
4. Brown, L.D.: Fundamentals of statistical exponential families: with applications in statistical decision theory. Institute of Mathematical Statistics, Hayworth (1986)
5. Amari, S., Nagaoka, H.: Methods of Information Geometry. Oxford University Press (2000)
6. Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. Journal of Machine Learning Research 6, 1705-1749 (2005)
7. Westover, M.: Asymptotic geometry of multiple hypothesis testing. IEEE Transactions on Information Theory 54(7), 3327-3329 (2008)
8. Boissonnat, J.D., Nielsen, F., Nock, R.: Bregman Voronoi diagrams. Discrete & Computational Geometry 44(2), 281-307 (2010)
9. Boissonnat, J.D., Yvinec, M.: Algorithmic Geometry. Cambridge University Press, New York (1998)
10. Nielsen, F.: k-MLE: A fast algorithm for learning statistical mixture models. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE (2012); preliminary, technical report on arXiv
11. Piro, P., Nielsen, F., Barlaud, M.: Tailored Bregman ball trees for effective nearest neighbors. In: European Workshop on Computational Geometry (EuroCG), LORIA, Nancy, France. IEEE (March 2009)
12. Nielsen, F., Piro, P., Barlaud, M.: Bregman vantage point trees for efficient nearest neighbor queries. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo (ICME), pp. 878-881 (2009)
13. Garcia, V., Debreuve, E., Nielsen, F., Barlaud, M.: k-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching. In: IEEE International Conference on Image Processing (ICIP), pp. 3757-3760 (2010)
14. Nielsen, F.: An information-geometric characterization of Chernoff information. IEEE Signal Processing Letters (SPL) 20(3), 269-272 (2013)
15. Nielsen, F., Boltz, S.: The Burbea-Rao and Bhattacharyya centroids. IEEE Transactions on Information Theory 57(8), 5455-5466 (2011)