Regularized latent semantic indexing

SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2011, Pages 685-694

  Wang, Quan ^a   Xu, Jun ^b   Li, Hang ^b   Craswell, Nick ^c  

^a PEKING UNIVERSITY   (China)

^b MICROSOFT RESEARCH   (United States)

^c MICROSOFT   (United States)

Author keywords

Regularization; Sparse methods; Topic modeling

Indexed keywords

INDEXING (OF INFORMATION); INFORMATION RETRIEVAL;

DOCUMENT COLLECTION; DOCUMENT REPRESENTATION; LATENT SEMANTIC INDEXING; QUADRATIC LOSS FUNCTIONS; REGULARIZATION; RELEVANCE RANKING; SPARSE METHODS; TOPIC MODELING;

SEMANTICS;

EID: 80052122601     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2009916.2010008     Document Type: Conference Paper

References (36)

1. L. AlSumait, D. Barbara, and C. Domeniconi. On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking. In ICDM, 2008.
2. A. Asuncion, P. Smyth, and M. Welling. Asynchronous distributed estimation of topic models for document analysis. Statistical Methodology, 2011.
3. D. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993-1022, 2003.
4. A. Buluc and J. R. Gilbert. Challenges and advances in parallel sparse matrix-matrix multiplication. In ICPP, pages 503-510, 2008.
5. C. J. Burges, R. Ragno, and Q. V. Le. Learning to rank with nonsmooth cost functions. In NIPS 19, 2007.
6. R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. Scope: Easy and efficient parallel processing of massive data sets. VLDB Endow., 1:1265-1276, 2008.
7. S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SISC, 20:33-61, 1998. (Pubitemid 128689501)
8. X. Chen, B. Bai, Y. Qi, Q. Lin, and J. Carbonell. Sparse latent semantic analysis. In NIPS Workshop, 2010.
9. J. Dean, S. Ghemawat, and G. Inc. Mapreduce: simplified data processing on large clusters. In OSDI, 2004.
10. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. J AM SOC INFORM SCI, 41:391-407, 1990.
11. C. Ding, T. Li, and W. Peng. On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing semantic indexing. COMPUT STAT DATA AN, 52:3913-3927, 2008.
12. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. ANN STAT, 32:407-499, 2004. (Pubitemid 41250302)
13. J. Friedman, T. Hastie, H. Hofling, and R. Tibshirani. Pathwise coordinate optimization. ANN APPL STAT, 1:302-332, 2007.
14. W. J. Fu. Penalized regressions: The bridge versus the lasso. J COMPUT GRAPH STAT, 7:397-416, 1998.
15. M. D. Hoffman, D. M. Blei, and F. Bach. Online learning for latent dirichlet allocation. In NIPS, 2010.
16. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50-57, 1999.
17. D. D. Lee and H. S. Seung. Learning the parts of objects with nonnegative matrix factorization. Nature, 401:391-407, 1999.
18. D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In NIPS 13, pages 556-562. 2001.
19. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS, pages 801-808. 2007.
20. C. Liu, H. chih Yang, J. Fan, L.-W. He, and Y.-M. Wang. Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce. In WWW, pages 681-690, 2010.
21. Z. Liu, Y. Zhang, and E. Y. Chang. Plda+: Parallel latent dirichlet allocation with data placement and pipeline processing. In TIST, 2010.
22. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS 21, pages 1033-1040. 2009.
23. D. M. Mimno and McCallum. Organizing the oca: Learning faceted subjects from a library of digital books. In JCDL, pages 376-385, 2007. (Pubitemid 350158008)
24. D. Newman, A. Asuncion, P. Smyth, and M. Welling. Distributed inference for latent dirichlet allocation. In NIPS, 2008.
25. B. A. Olshausen and D. J. Fieldt. Sparse coding with an overcomplete basis set: a strategy employed by v1. VISION RES, 37:3311-3325, 1997. (Pubitemid 27493805)
26. M. Osborne, B. Presnell, and B. Turlach. A new approach to variable selection in least squares problems. IMA J NUMER ANAL, 2000.
27. S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In TREC'3, 1994.
28. R. Rubinstein, M. Zibulevsky, and M. Elad. Double sparsity: Learning sparse dictionaries for sparse signal approximation. IEEE T SIGNAL PROCES, pages 1553-1564, 2008.
29. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18:613-620, 1975.
30. A. P. Singh and G. J. Gordon. A unified view of matrix factorization models. In ECMLPKDD, pages 358-373, 2008.
31. A. Smola and S. Narayanamurthy. An architecture for parallel topic models. Proc. VLDB Endow., 3:703-710, 2010.
32. R. Thakur and R. Rabenseifner. Optimization of collective communication operations in mpich. INT J HIGH PERFORM C, 19:49-66, 2005. (Pubitemid 40329106)
33. C. Wang and D. M. Blei. Decoupling sparsity and smoothness in the discrete hierachical dirichlet process. In NIPS, 2009.
34. Y. Wang, H. Bai, M. Stanton, W. yen Chen, and E. Y. Chang. Plda: Parallel latent dirichlet allocation for large-scale applications. In AAIM, pages 301-314, 2009.
35. X. Wei and B. W. Croft. Lda-based document models for ad-hoc retrieval. In SIGIR, pages 178-185, 2006.
36. F. Yan, N. Xu, and Y. A. Qi. Parallel inference for latent dirichlet allocation on graphics processing units. In NIPS, pages 2134-2142, 2009.