Integrating document clustering and topic modeling

Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013

Volumn , Issue , 2013, Pages 694-703

  Xie, Pengtao ^a   Xing, Eric P ^b  

^a TSINGHUA UNIVERSITY   (China)

^b Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DOCUMENT CLUSTERING; DOCUMENT COLLECTION; HIDDEN VARIABLE; MIXTURE COMPONENTS; MODEL PARAMETERS; PROJECT DOCUMENTS; UNIFIED FRAMEWORK; VARIATIONAL INFERENCE;

ARTIFICIAL INTELLIGENCE; INFORMATION RETRIEVAL;

CLUSTER ANALYSIS;

EID: 84888158949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Charu C Aggarwal and ChengXiang Zhai. A survey of text clustering algorithms. Mining Text Data, pages 77-128, 2012.
2. Amr Ahmed and Eric P Xing. Staying informed: supervised and semi-supervised multi-view topical analysis of ideological perspective. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1140-1150. Association for Computational Linguistics, 2010.
3. David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. the Journal of Machine Learning Research, 3: 993-1022, 2003.
4. Jonathan Boyd-Graber, Jordan Chang, Sean Gerrish, Chong Wang, and David Blei. Reading tea leaves: how humans interpret topic models. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, 2009.
5. Deng Cai, Xiaofei He, and Jiawei Han. Locally consistent concept factorization for document clustering. Knowledge and Data Engineering, IEEE Transactions on, 23(6): 902-913, 2011.
6. Chaitanya Chemudugunta and Padhraic Smyth Mark Steyvers. Modeling general and specific aspects of documents with a probabilistic topic model. In Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, Volume 19, page 241. MIT Press, 2007.
7. Scott Deerwester, Susan T. Dumais, George W Furnas, Thomas K Landauer, and Richard Harshman. Indexing by latent semantic analysis. Journal of the American society for Information Science, 41(6): 391-407, 1990.
8. Li Fei-Fei and Pietro Perona. A bayesian hierarchical model for learning natural scene categories. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Volume 2, pages 524-531. IEEE, 2005.
9. Thomas Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1): 177-196, 2001.
10. Yue Lu, Qiaozhu Mei, and ChengXiang Zhai. Investigating task performance of probabilistic topic models: an empirical study of plsa and lda. Information Retrieval, 14(2): 178-203, 2011.
11. Andrew Y Ng, Michael I Jordan, Yair Weiss, et al. On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems, 2: 849-856, 2002.
12. Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8): 888-905, 2000.
13. Ivan Titov and Ryan McDonald. Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web, pages 111-120. ACM, 2008.
14. Martin J Wainwright and Michael I Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends@ in Machine Learning, 1(1-2): 1-305, 2008.
15. Hanna M Wallach. Structured topic models for language. Unpublished doctoral dissertation, Univ. of Cambridge, 2008.
16. Wei Xu and Yihong Gong. Document clustering by concept factorization. In Proceedings of the 27th annual international ACM SIGIR conference on Research and Development in Information Retrieval, pages 202-209. ACM, 2004.
17. Wei Xu, Xin Liu, and Yihong Gong. Document clustering based on non-negative matrix factorization. In Proceedings of the 26th annual international ACM SIGIR conference on Research and Development in Informaion Retrieval, pages 267-273. ACM, 2003.
18. Jun Zhu, Li-Jia Li, Li Fei-Fei, and Eric P Xing. Large margin learning of upstream scene understanding models. Advances in Neural Information Processing Systems, 24, 2010.