DiscLDA: Discriminative learning for dimensionality reduction and classification

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 897-904

  Lacoste Julien, Simon ^a   Sha, Fei ^b   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFORMATION RETRIEVAL SYSTEMS; MAXIMUM LIKELIHOOD; MIXTURES; STATISTICS;

DIMENSIONALITY REDUCTION; DISCRIMINATIVE LEARNING; LATENT DIRICHLET ALLOCATION; LATENT STRUCTURES; MIXTURE PROPORTIONS; PREDICTIVE POWER; PROBABILISTIC TOPIC MODELS; SIDE INFORMATION; TEXT DOCUMENT; TEXT IMAGES;

LINEAR TRANSFORMATIONS;

EID: 79957489009     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. T. L. Berg, A. C. Berg, J. Edwards, M. Maire, R. White, Y. W. Teh, E. Learned-Miller, and D. A. Forsyth. Names and faces in the news. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Washington, DC, 2004.
2. D. Blei and J. McAuliffe. Supervised topic models. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2008.MIT Press.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
4. F. Chiaromonte and R. D. Cook. Sufficient dimension reduction and graphics in regression. Annals of the Institute of Statistical Mathematics, 54(4):768-795, 2002.
5. L. Fei-fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
6. P. Flaherty, G. Giaever, J. Kumm, M. I. Jordan, and A. P. Arkin. A latent variable model for chemogenomic profiling. Bioinformatics, 21:3286-3293, 2005. (Pubitemid 41418443)
7. K. Fukumizu, F. R. Bach, and M. I. Jordan. Kernel dimension reduction in regression. Annals of Statistics, 2008. To appear.
8. T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101:5228-5235, 2004. (Pubitemid 38469131)
9. D. Mimno and A. McCallum. Topic models conditioned on arbitrary features with Dirichletmultinomial regression. In Proceedings of the 24th Annual Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, 2008.
10. M. Rosen-Zvi, T. Griffiths T, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence, Banff, Canada, 2004.
11. L. J. P. van der Maaten and G. E. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008.