Mixed bregman clustering with approximation guarantees

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

Volumn 5212 LNAI, Issue PART 2, 2008, Pages 154-169

  Nock, Richard ^a   Luosto, Panu ^b   Kivinen, Jyrki ^b  

^a UNIVERSITÉ DES ANTILLES ET DE LA GUYANE   (France)

^b UNIVERSITY OF HELSINKI   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FLOW OF SOLIDS; LEARNING SYSTEMS; POLYNOMIAL APPROXIMATION; ROBOT LEARNING;

BREGMAN DIVERGENCES; EUCLIDEAN; INITIALIZATION STEPS; K-MEANS CLUSTERING; SEEDING TECHNIQUES;

CLUSTERING ALGORITHMS;

EID: 56049127972     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87481-2_11     Document Type: Conference Paper

References (15)

1. th ACM-SIAM Symposium on Discrete Algorithms, pp. 799-808 (2008)
2. th ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035 (2007)
3. Azoury, K.S., Warmuth, M.K.: Relative loss bounds for on-line density estimation with the exponential family of distributions. Machine Learning Journal 43(3). 211-246 (2001)
4. Banerjee. A., Guo. X., Wang. H.: On the optimality of conditional expectation as a Bregman predictor. IEEE Trans. on Information Theory 51, 2664-2669 (2005)
5. Banerjee, A.. Merugu, S.. Dhillon, L, Ghosh, J.: Clustering with Bregman divergences. Journal of Machine Learning Research 6, 1705-1749 (2005)
6. Bregman. L.M.: The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR. Comp. Math, and Math. Phys. 7, 200-217 (1967)
7. Crammer, K., Kearns, M., Wortman, J.: Learning from multiple sources. In: Advances in Neural Information Processing Systems 19, pp. 321-328. MIT Press, Cambridge (2007)
8. st Conference on Learning Theory (2008)
9. Deza. E.. Deza, M.-M.: Dictionary of distances. Elsevier, Amsterdam (2006)
10. Gentile, C.: The robustness of the p-norm algorithms. Machine Learning Journal 53(3), 265-299 (2003)
11. Lloyd, S.: Least squares quantization in PCM. IEEE Trans, on Information Theory 28, 129-136 (1982)
12. th ACM-SIAM Symposium on Discrete Algorithms, pp. 746-755 (2007)
13. Nock, R., Nielsen, F.: Fitting the smallest enclosing Bregman ball. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M.. Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 649-656. Springer, Heidelberg (2005)
14. th IEEE Symposium on the Foundations of Computer Science, pp. 165-176. IEEE Computer Society Press, Los Alamitos (2006)
15. Veldhuis, R.: The centroid of the symmetrical Kullback-Leibler distance. IEEE Signal Processing Letters 9, 96-99 (2002)