A rate-distortion one-class model and its applications to clustering

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 184-191

  Crammer, Koby ^a   Talukdar, Partha Pratim ^a   Pereira, Fernando ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC DISTORTION; MACHINE LEARNING; LEARNING SYSTEMS; MOTION ESTIMATION; ROBOT LEARNING;

BACKGROUND CLUTTER; CLASS MODELING; CLUSTER PURITIES; ITS APPLICATIONS; ONE-CLASS CLASSIFICATION; POSITIVE EXAMPLES; RATE DISTORTIONS;

SIGNAL DISTORTION;

BACKGROUND CLUTTERS; CLASS MODELS; EFFICIENT ALGORITHMS; ONE-CLASS CLASSIFICATIONS; POSITIVE EXAMPLES; RATE DISTORTIONS;

EID: 56449109767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390180     Document Type: Conference Paper

References (15)

1. Bekkerman, R., & McCallum, A. (2005). Disambiguating web appearances of people in a social network. WWW. Chiba, Japan.
2. Ben-Hur, A., Horn, D., Siegelmann, H., & Vapnik, V. (2001). Support vector clustering. JMLR, 2, 125-137.
3. Censor, Y., & Zenios, S. (1997). Parallel optimization: Theory, algorithms, and applications. Oxford Univ. Press, NY, USA.
4. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Wiley.
5. Crammer, K., & Chechik, G. (2004). A. needle in a haystack: Local one-class optimization. ICML 23.
6. Crammer, K., & Singer, Y. (2003). Learning algorithms for enclosing points in bregmanian spheres. COLT 16.
7. Gupta, G., & Ghosh, J. (2006). Bregman bubble clustering: A robust, scalable framework for locating multiple, dense regions in data. ICDM.
8. Lashkari, D., & Golland, P. (2008). Convex clustering with exemplar-based models. NIPS.
9. Schölkopf, B., Burges, C., & Vapnik, V. (1995). Extracting support data for a given task. KDD 1.
10. Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13, 1443-1472.
11. Slonini, N. (2003), The information bottleneck: Theory and applications. Doctoral dissertation, Hebrew University.
12. Slonim, N., Friedman, N., & Tishby, N. (2002). Unsupervised document classification using sequential information maximization. SIGIR.
13. Slonim, N., & Weiss, Y. (2002). Maximum likelihood and the information bottleneck. NIPS.
14. Tax, D., & Duin, R. (1999). Data domain description using support vectors. ESANN (pp. 251-256).
15. Tishby, N., Pereira, F., & Bialek, W. (1999). The information bottleneck method. 37th Allerton Conference on Communication, Control, and Computing. Allerton House, Illinois.