Cross-instance tuning of unsupervised document clustering algorithms

NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

Volumn , Issue , 2007, Pages 252-259

  Karakos, Damianos ^a   Eisner, Jason ^a   Khudanpur, Sanjeev ^a   Priebe, Carey E ^a  

^a Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERINGS; DATA SETS; DEGREE OF FREEDOM; DOCUMENT CLUSTERING; DOCUMENT COLLECTION; GENERALIZED MUTUAL INFORMATION; NEWSGROUPS; TEST COLLECTION; TEST SETS; UNSUPERVISED ALGORITHMS;

COMPUTATIONAL LINGUISTICS; INFORMATION RETRIEVAL; INFORMATION THEORY; STRAPPING; UNSUPERVISED LEARNING;

CLUSTERING ALGORITHMS;

EID: 80052989807     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. R. K. Ando and T. Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, Nov.
2. S. Ben-David and R. Schuller. 2003. Exploiting task relatedness for multiple task learning. In Proc. of the Sixteenth Annual Conference on Learning Theory (COLT-03).
3. A. Blum and T. Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the Workshop on Computational Learning Theory (COLT-98), pages 92-100.
4. R. Caruana. 1997. Multitask learning. Machine Learning, 28(1):41-75. (Pubitemid 127507169)
5. T. Cover and J. Thomas. 1991. Elements of Information Theory. John Wiley and Sons.
6. I. Csiszár. 1995. Generalized cutoff rates and Rényi's information measures. IEEE Trans. on Information Theory, 41(1):26-34, January.
7. I. Dhillon, S. Mallela, and R. Kumar. 2003. A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research (JMLR), Special Issue on Variable and Feature Selection, pages 1265-1287, March.
8. J. Eisner and D. Karakos. 2005. Bootstrapping without the boot. In Proc. 2005 Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), October.
9. T. Evgeniou and M. Pontil. 2004. Regularized multi-task learning. In Proc. Knowledge Discovery and Data Mining.
10. C. Fraley and A. E. Raftery. 1999. Mclust: Software for model-based cluster analysis. Journal on Classification, 16:297-306.
11. M. Gatford, M. M. Hancock-Beaulieu, S. Jones, S. Walker, and S. E. Robertson. 1995. Okapi at TREC-3. In The Third Text Retrieval Conference (TREC-3), pages 109-126.
12. A. Ben Hamza and H. Krim. 2003. Jensen-Rényi divergence measure: Theoretical and computational perspectives. In Proc. IEEE Int. Symp. on Information Theory, Yokohama, Japan, June.
13. T. Hastie, R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning. Springer-Verlag.
14. A. O. Hero, B. Ma, O. Michel, and J. Gorman. 2001. Alpha-divergence for classification, indexing and retrieval. Technical Report CSPL-328, University of Michigan Ann Arbor, Communications and Signal Processing Laboratory, May.
15. F. Jelinek. 1997. Statistical Methods for Speech Recognition. MIT Press.
16. T. Joachims. 2002. Optimizing search engines using clickthrough data. In ACM Conf. on Knowledge Discovery and Data Mining (KDD).
17. D. Karakos, S. Khudanpur, J. Eisner, and C. E. Priebe. 2005. Unsupervised classification via decision trees: An information-theoretic perspective. In Proc. 2005 International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), March.
18. D. Karakos, S. Khudanpur, J. Eisner, and C. E. Priebe. 2007a. Information-theoretic aspects of iterative denoising. Submitted to the Journal of Machine Learning Research, February.
19. D. Karakos, S. Khudanpur, J. Eisner, and C. E. Priebe. 2007b. Iterative denoising using Jensen-Rényi divergences with an application to unsupervised document categorization. In Proc. 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), April.
20. K. Lang. 1995. Learning to filter netnews. In Proc. 13th Int. Conf. on Machine Learning, pages 331-339.
21. David D. Lewis and Philip J. Hayes. 1994. Guest editorial. ACM Transactions on Information Systems, 12(3):231, July.
22. C. E. Priebe, D. J. Marchette, and D. M. Healy. 2004a. Integrated sensing and processing decision trees. IEEE Trans. on Pat. Anal. and Mach. Intel., 26(6):699-708, June.
23. C. E. Priebe, D. J. Marchette, Y. Park, E. Wegman, J. Solka, D. Socolinsky, D. Karakos, K. Church, R. Guglielmi, R. Coifman, D. Lin, D. Healy, M. Jacobs, and A. Tsao. 2004b. Iterative denoising for cross-corpus discovery. In Proc. 2004 International Symposium on Computational Statistics (COMPSTAT 2004), August.
24. N. Slonim and N. Tishby. 2000. Document clustering using word clusters via the information bottleneck method. In Research and Development in Information Retrieval, pages 208-215.
25. N. Slonim, N. Friedman, and N. Tishby. 2002. Unsupervised document classification using sequential information maximization. In Proc. SIGIR'02, 25th ACM Int. Conf. on Research and Development of Inform. Retrieval.
26. N. Slonim. 2003. IBA 1.0: Matlab code for information bottleneck clustering algorithms. Available from http://www.princeton.edu/~nslonim/IB- Release1.0/IB-Release1-0.tar.
27. N. Tishby, F. Pereira, and W. Bialek. 1999. The information bottleneck method. In 37th Allerton Conference on Communication and Computation.
28. K. Torkkola. 2002. On feature extraction by mutual information maximization. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP-2002), May.
29. Y. Yang and J. Pedersen. 1997. A comparative study on feature selection in text categorization. In Intl. Conf. on Machine Learning (ICML-97), pages 412-420.
30. D. Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proc. 33rd Annual Meeting of the Association for Computational Linguistics, pages 189-196, Cambridge, MA.