Discriminative mixed-membership models

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 466-475

  Shan, Hanhuai ^a   Banerjee, Arindam ^a   Oza, Nikunj C ^b  

^a University of Minnesota   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; CLASS LABELS; FEATURE VECTORS; LATENT DIRICHLET ALLOCATION; LOGISTIC REGRESSIONS; MEAN-FIELD; MEMBERSHIP MODELS; MULTI-CLASS; NAIVE BAYES MODELS; NUMBER OF COMPONENTS; STATE OF THE ART; TEXT CLASSIFICATION; VARIATIONAL APPROXIMATION; VARIATIONAL INFERENCE;

APPROXIMATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); INFERENCE ENGINES; LEARNING ALGORITHMS;

TEXT PROCESSING;

EID: 77951199659     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.58     Document Type: Conference Paper

References (19)

1. E. Airoldi, D. Blei, S. Fienberg, and E. Xing. Mixed membership stochastic blockmodels. JMLR, 9:1823-1856, 2008.
2. A. Banerjee and H. Shan. Latent Dirichlet conditional naive Bayes models. In ICDM, 2007.
3. O. Barndorff-Nielsen. Information and Exponential Families in Statistical Theory. John Wiley and Sons, 1978.
4. D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1):121-144, 2006.
5. D. Blei and J. McAuliffe. Supervised topic models. In NIPS, 2007.
6. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993-1022, 2003.
7. C. Burges. A tutorial on suppoert vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121-167, 1998.
8. C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
9. P. Domingos and M. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning Journal, 29:103-130, 1997.
10. P. Flaherty, G. Giaever, M. Jordan, and A. Arkin. A latent variable model for chemogenomic profiling. Bioinformatics, 21:3286-3293, 2005.
11. T. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101:5228-5225, 2004.
12. P. Koutsourelakis and T. Eliassi-Rad. Finding mixedmemberships in social networks. In AAAI, 2008.
13. S. Lacoste-Julien, F. Sha, and M. Jordan. DiscLDA: Discriminative learning for dimensionality reduction and classification. In NIPS, 2008.
14. P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman & Hall/CRC, 1989.
15. D. Mimno and A. McCallum. Topic models conditioned on arbitrary features with dirichlet-multinomial regression. In UAI, 2008.
16. T. Minka. A comparison of numerical optimizers for logistic regression. Technical report, 2003.
17. F. Pampel. Logistic Regression: A Primer. Sage, 2000.
18. C. Wang, D. Blei, and L. Fei-Fei. Simultaneous image classificationn and annotation. In CVPR, 2009.
19. H. Wang, M. Huang, and X. Zhu. A generative probabilistic model for multi-label classification. In ICDM, 2008.