Mixture of Watson distributions: A generative model for hyperspherical embeddings

Journal of Machine Learning Research

Volumn 2, Issue , 2007, Pages 35-42

  Bijral, Avleen S ^a   Breitenbach, Markus ^a   Grudic, Greg ^a  

^a University of Colorado at Boulder   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENT SHAPES; DIRECTIONAL DATA; EMBEDDING DIMENSIONS; EMBEDDINGS; EXPECTATION MAXIMIZATION; GENERATIVE MODEL; HYPERSPHERICAL; MACHINE LEARNING APPLICATIONS; MODELING CAPABILITIES; NUMERICAL APPROXIMATIONS; ORIGINAL STRUCTURES; REAL DATA SETS; SPECTRAL CLUSTERING; SUB-DOMAINS; UNIT VECTORS; VON MISES-FISHER DISTRIBUTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOINFORMATICS;

IMAGE SEGMENTATION;

EID: 84862277789     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (13)

1. M. Meila and J. Shi. A random walks view of spectral segmentation. In Proceedings of the 7th International Conference on Artificial Intelligence and Statistics, 2001.
2. Band M. and Huang K. A unifying theorem for spectral embedding and clustering. In C. M. Bishop and B. J. Frey, editors, Proceedings of the 9th International Conference on Artificial Intelligence and Statistics, January 2003.
3. A. Banerjee, I. S. Dhillon, Ghosh J., and Sra S. Clustering on the unit hypersphere using von mises-fisher distributions. Journal of Machine Learning Research, 6:1345-1382, Sep 2005.
4. K.V. Mardia and P. Jupp. DirectionalStatistics. John Wiley and Sons Ltd., 2000.
5. Dhillon I.S. and Modha D.S. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1):143-175, 2001.
6. Abramowitz M. and Stegun I.A. Handbook of Mathematical Functions. Dover Publ. Inc., New York, 1974.
7. Barrowes B. Matlab routines for computation of special functions. http://ceta.mit.edu/compspec-func/.
8. A. Y. Ng, M. I. Jordan, and Y.Weiss. On spectral clustering: Analysis and an algorithm. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
9. Bilmes J. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report ICSI-TR-97-021, University of California, 1997.
10. W. Gautschi. Anomalous convergence of a continued fraction for ratios of kummer functions. Mathematics of Computation, (31):994-999, 1977.
11. R. Horn and C. Johnson. Matrix Analysis. Cambridge University Press, 1999.
12. Andrew Kachites McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/ mccallum/bow, 1996.
13. D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In L. Saul S. Thrun and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, Cambridge, Mass., 2004. MIT Press.