A nonparametric information theoretic clustering algorithm

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 351-358

  Faivishevsky, Lev ^a   Goldberger, Jacob ^a  

^a BAR ILAN UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER DISTRIBUTIONS; DATA POINTS; DATA SETS; INFORMATION THEORETIC CLUSTERING ALGORITHMS; MUTUAL INFORMATIONS; NON-PARAMETRIC; NON-PARAMETRIC ESTIMATIONS; PARAMETRIC MODELS;

INFORMATION THEORY; LEARNING SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 77956520100     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Asuncion, A. and Newman, D.J. UCI machine learning repository. 2007. URL http://www.ics.uci.edu/~mlearn/MLRepository.Html.
2. Banerjee, A., Merugu, S., Dhillon, I. and Ghosh, J. Clustering with Bregman divergences. In Journal of Machine Learning Research, pp. 1705-1749, 2004.
3. Davis, J. V. and Dhillon, I. Differential entropie clustering of multivariate Gaussians. Advances in Neural Information Processing Systems 19, 2007.
4. Dhillon, I., Mallela, S., Kumar, R., Mallela, S., Guyon, I. and Elisseeff, A. A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research, 3:2003, 2003.
5. Faivishevsky, L. and Goldberger, J. ICA based on a smooth estimation of the differential entropy. Advances in Neural Information Processing Systems 21, 2009.
6. Goldberger, J. and Roweis, S. Hierarchical clustering of a mixture model. Advances in Neural Information Processing Systems 17, 2005.
7. Goria, M., Leonenko, N., Mergel, V. and Inverardi, P. A new class of random vector entropy estimators and its applications in testing statistical hypotheses. J. Non-param. Statist, pp. 277-297, 2005.
8. Kozachenko, L. and Leonenko, N. On statistical estimation of entropy of random vector. Problems Infor. Trans-miss., 23 (2), 1987.
9. Lloyd, S. P. Least squares quantization in pcm. Special issue on quantization, IEEE Trans. Inform. Theory, pp. 129-137, 1982.
10. Ng, A. Y., Jordan, M. and Weiss, Y. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 14, 2002.
11. Rand, W. Objective criteria for the evaluation of clustering methods. J. Amer.Statist. Assoc. 66, pp. 846-850, 1971.
12. Salton, G. and McGill, M. J. Introduction to modern information retrieval. McGraw-Hill Book Company, 1983.
13. Seldin, Y., Slonim, N. and Tishby, N. Information bottleneck for non co-occurrence data. Advances in Neural Information Processing Systems 19, 2007.
14. Shental, N., Bar-Hillel, A., Hertz, T. and Weinshall, D. Computing gaussian mixture models with EM using equivalence constraints. Advances in Neural Information Processing Systems (NIPS), 2004.
15. Slonim, N. and Tishby, N. Document clustering using word clusters via the information bottleneck method. Proc. of the 23rd Ann. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2000.
16. Slonim, N., Friedman, N. and Tishby, N. Unsupervised document classification using sequential information maximization. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2002.
17. Slonim, N., Atwal, G. S., Tracik, G. and Bialek, W. Information-based clustering. Proc. of the National Academy of Science (PNAS), 102:182971830, 2005.
18. Tishby, N. and Slonim, N. Data clustering by markovian relaxation and the information bottleneck method. Advances in Neural Information Processing Systems 13, 2001.
19. Tishby, N., Pereira, F. and Bialek, W. The information bottleneck method. Allerton Conf. on Communication, Control and Computing, 1999.
20. Torkkola, K. Feature extraction by non-parametric mutual informtaion maximization. Journal of Machine Learning Research, 2003.
21. Victor, J. D. Binless strategies for estimation of information from neural data. Physical Review, 66, 2002.
22. Wang, Q., Kulkarni, S. R. and Verdu, S. Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans. Information Theory, pp. 2392-2405, 2009.
23. Zelnik-Manor, L. and Perona, P. Self-tuning spectral clustering. Advances in Neural Information Processing Systems 17, 2005.