Modeling hidden topics on document manifold

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 911-920

  Cai, Deng ^a   Mei, Qiaozhu ^a   Han, Jiawei ^a   Zhai, Chengxiang ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Document representation; Generative model; Manifold regularization; Probabilistic latent semantic indexing

Indexed keywords

DOCUMENT ANALYSIS; DOCUMENT REPRESENTATION; EUCLIDEAN; EUCLIDEAN SPACES; GENERATIVE MODEL; JOINT PROBABILITY; KEY PROBLEMS; LAPLACIANS; LATENT DIRICHLET ALLOCATIONS; MANIFOLD REGULARIZATION; NOVEL ALGORITHM; OVERFITTING; PROBABILISTIC LATENT SEMANTIC INDEXING; SEMANTIC STRUCTURES; TEXT DATA;

ALGORITHMS; INDEXING (OF INFORMATION); KNOWLEDGE MANAGEMENT; RANDOM PROCESSES; RANDOM VARIABLES; SEMANTICS;

PROBABILITY DISTRIBUTIONS;

EID: 70349247055     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458082.1458202     Document Type: Conference Paper

References (24)

1. R. Ando. Latent semantic space: Iterative scaling improves precision of inter-document similarity measurement. In Proc. 2000 Int. Conf. on Research and Development in Information Retrieval (SIGIR'00), Athens, Greece, July 2000.
2. M. Belkin. Problems of Learning on Manifolds. PhD thesis, University of Chicago, 2003.
3. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS 14, 2001.
4. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
5. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of machine Learning Research, 2003.
6. D. Cai, X. He, and J. Han. Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering, 17(12):1624-1637, December 2005.
7. F. R. K. Chung. Spectral Graph Theory, volume 92 of Regional Conference Series in Mathematics. AMS, 1997.
8. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
9. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1-38, 1977.
10. X. He, D. Cai, H. Liu, and W.-Y. Ma. Locality preserving indexing for document representation. In Proc. 2004 Int. Conf. on Research and Development in Information Retrieval (SIGIR'04), pages 96-103, Sheffield, UK, July 2004.
11. T. Hofmann. Probabilistic latent semantic indexing. In Proc. 1999 Int. Conf. on Research and Development in Information Retrieval (SIGIR'99), 1999.
12. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1-2):177-196, 2001.
13. L. Lovasz and M. Plummer. Matching Theory. Akadémiai Kiadó, North Holland, Budapest, 1986.
14. R. Neal and G. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models. Kluwer, 1998.
15. A. Y. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14, pages 849-856. MIT Press, Cambridge, MA, 2001.
16. A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In 17th Conference on Uncertainty in Artificial Intelligence, pages 437-444, 2001.
17. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1992.
18. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
19. L. Si and R. Jin. Adjusting mixture weights of gaussian mixture model via regularized probabilistic latent semantic analysis. In The Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), 2005.
20. X. Wang, J.-T. Sun, Z. Chen, and C. Zhai. Latent semantic analysis for multiple-type interrelated data objects. In Proc. 2006 Int. Conf. on Research and Development in Information Retrieval (SIGIR'06), 2006.
21. W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In Proc. 2003 Int. Conf. on Research and Development in Information Retrieval (SIGIR'03), 2003.
22. H. Zha, C. Ding, M. Gu, X. He, , and H. Simon. Spectral relaxation for k-means clustering. In Advances in Neural Information Processing Systems 14, pages 1057-1064. MIT Press, Cambridge, MA, 2001.
23. D. Zhang, X. Chen, and W. S. Lee. Text classification with kernels on the multinomial manifold. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 266-273, 2005.
24. X. Zhu and J. Lafferty. Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. In ICML '05: Proceedings of the 22nd international conference on Machine learning, pages 1052-1059, 2005.