Discriminative topic modeling based on manifold learning

ACM Transactions on Knowledge Discovery from Data

Volumn 5, Issue 4, 2012, Pages

  Huh, Seungil ^a   Fienberg, Stephen E ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Dimensionality reduction; Document clustering and classification; Semisupervised learning; Topic modeling

Indexed keywords

DIMENSIONALITY REDUCTION; DISCRIMINATING POWER; DOCUMENT CLUSTERING; GENERALIZED EM ALGORITHMS; HIDDEN STRUCTURES; LAPLACIANS; LATENT DIRICHLET ALLOCATIONS; LOCAL CONSISTENCY; LOCAL MANIFOLD STRUCTURE; MANIFOLD LEARNING; MODEL FITTING; NONLINEAR DIMENSIONALITY REDUCTION; PARETO IMPROVEMENTS; PROBABILISTIC LATENT SEMANTIC ANALYSIS; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; TEXT CORPORA; TEXT DOCUMENT; TOPIC MODEL; UNSUPERVISED CLUSTERING;

CHARACTER RECOGNITION; STATISTICS;

TEXT PROCESSING;

EID: 84857717020     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/2086737.2086740     Document Type: Conference Paper

References (30)

1. BARR, N. 2004. Economics of the Welfare State. Oxford University Press, Oxford, UK.
2. BELKIN, M. AND NIYOGI, P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 586-691.
3. BELKIN, M., NIYOGI, P., AND SINDHWANI, V. 2006. Mainfold regularization: A geometric framework for learning from examples. J. Mach. Learn. 7, 2399-2434. (Pubitemid 44708005)
4. BENGIO, Y., PAIEMENT, J., VINCENT, P., DELALLEAU, O., LE ROUX, N., AND OUIMET, M. 2004. Out-of-sample extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
5. BLEI, D. AND MCAULIFFE, J. 2008. Supervised topic models. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 121-128.
6. BLEI, D. M., NG, A. Y., AND JORDAN, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. 3, 993-1022.
7. BOLEY, D. L. 1998. Principal direction divisive partitioning. Data Mining Knowl. Discov. 2, 4, 325-344.
8. CAI, D., MEI, Q., HAN, J., AND ZHAI, C. 2008. Modeling hidden topics on document manifold. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM). 911-920.
9. CAI, D., WANG, X., AND HE, X. 2009. Probabilistic dyadic data analysis with local and global consistency. In Proceedings of the International Conference on Machine Learning (ICML). 105-112.
10. CHUNG, F. R. K. 1997. Spectral Graph Theory. American Mathematical Society.
11. DEERWESTER, S. C., DUMAIS, S. T., LANDAUER, T. K., FURNAS, G. W., AND HARSHMAN, R. A. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inf. Sci. 41, 391-407.
12. DEMPSTER, A. P., LAIRD, N. M., AND RUBIN, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Series B (Methodological) 39, 1-38.
13. HINTON, G. AND ROWEIS, S. 2002. Stochastic neighbor embedding. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 833-840.
14. HOFMANN, T. 1999. Probabilistic latent semantic indexing. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 50-57.
15. HUH, S. AND FIENBERG, S. E. 2010. Discriminative topic modeling based on manifold learning. In Proceedings of the ACM SIGKDD Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). 653-661.
16. JOLLIFFE, I. T. 2002. Principal Component Analysis 2nd Ed. Springer Series in Statistics, Vol. 29, Springer, NY.
17. KUHN, H. W. 1955. The Hungarian method for the assignment problem. Naval Res. Logist. Quarterly, 83-97.
18. LACOSTE-JULIEN, S., SHA, F., AND JORDAN, M. I. 2008. DiscLDA: Discriminative learning for dimensionality reduction and classification. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 897-904
19. LEE, D. D. AND SEUNG, H. S. 2000. Algorithms for non-negative matrix factorization. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 556-562.
20. ROWEIS, S. AND SAUL, L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326. (Pubitemid 32041578)
21. SALTON, G. AND BUCKLEY, C. 1988. Term-weighting approaches in automatic text retrieval. Inform. Proc. Manage. 24, 513-523.
22. SHA, F., SAUL, L. K., AND LEE, D. D. 2003. Multiplicative updates for nonnegative quadratic programming in support vector machines. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 1041-1048.
23. TENENBAUM, J. B., DE SILVA, V., AND LANGFORD, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323. (Pubitemid 32041577)
24. TROSSET, M. W., PRIEBE, C. E., PARK, Y., AND MILLER, M. I. 2008. Semisupervised learning from dissimilarity data. Comput. Statist. Data Anal. 52, 10, 4643-4657.
25. VAN DER MAATEN, M. AND HILTON, G. 2008. Visualizing data using T-SNE. J. Mach. Learn. Res. 9, 2579-2605.
26. XU, W., LIU, X., AND GONG, Y. 2003. Document clustering based on non-negative matrix factorization. In Proceedings of the Annual ACM Conference on Research and Development in Information Retrieval (SIGIR). 267-273.
27. YAN, S., XU, D., ZHANG, B., ZHANG, H.-J., YANG, Q., AND LIN, S. 2007. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1, 40-51. (Pubitemid 46415944)
28. ZHOU, D., BOUSQUET, O., LAL, T. N., WESTON, J., AND SCHöLKOPF, B. 2003. Learning with local and global consistency. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). 321-328.
29. ZHU, J., AHMED, A., AND XING, E. P. 2009. MedLDA: Maximum margin supervised topic models for regression and classification. In Proceedings of the International Conference on Machine Learning (ICML).
30. ZHU, X., GHAHRAMANI, Z., AND LAFFERTY, J. D. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the International Conference on Machine Learning (ICML). 912-919.