Reconceptualizing the classification of PNAS articles

Proceedings of the National Academy of Sciences of the United States of America

Volumn 107, Issue 49, 2010, Pages 20899-20904

  Airoldi, Edoardo M ^a   Erosheva, Elena A ^b   Fienberg, Stephen E ^c,d   Joutard, Cyrille ^e   Love, Tanzy ^f   Shringarpure, Suyash ^d  

^a HARVARD UNIVERSITY   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

^d Place Eugène Bataillon ^*  (United States)

^e UNIV MONTPELLIER   (France)

^f UNIVERSITY OF ROCHESTER MEDICAL CENTER   (United States)

Author keywords

Dirichlet process; Hierarchical modeling; Monte Carlo Markov chain; Text analysis; Variational inference

Indexed keywords

ARTICLE; MACHINE LEARNING; PRIORITY JOURNAL; PUBLICATION; PUBLISHING; SCIENTIFIC LITERATURE; STATISTICAL ANALYSIS; STATISTICAL MODEL;

CLASSIFICATION; METHODS; NATIONAL ACADEMY OF SCIENCES (U.S.); PERIODICALS AS TOPIC; PUBLICATIONS; STATISTICS AS TOPIC; UNITED STATES;

EID: 78650497230     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1013452107     Document Type: Article

References (20)

1. Shiffrin RM, Börner K (2004) Mapping knowledge domains. Proc Natl Acad Sci USA 101:5183-5185.
2. Erosheva EA, Fienberg SE, Lafferty J (2004) Mixed-membership models of scientific publications. Proc Natl Acad Sci USA 101:5220-5227.
3. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci USA 101:5228-5235.
4. Erosheva EA, Fienberg SE (2005) Classification-The Ubiquitous Challenge, eds CWeihs and W Gaul (Springer, Berlin), pp 11-26.
5. Jordan MI, Ghahramani Z, Jaakkola T, Saul L (1999) Introduction to variational methods for graphical models. Mach Learn 37:183-233.
6. Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12):e252.
7. Erosheva EA, Fienberg SE, Joutard C (2007) Describing disability through individuallevel mixture models for multivariate binary data. Ann Appl Stat 1:502-537.
8. Hastie T, Tibshirani R, Friedman JH (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York).
9. Ferguson TS (1973) A Bayesian analysis of some nonparametric problems. Ann Stat 1:209-230.
10. Neal R (2000) Markov chain sampling methods for Dirichlet process mixture models. J Comput Graph Stat 9:249-265.
11. Schwarz GE (1978) Estimating the dimension of a model. Ann Stat 6:461-464.
12. Blei DM, Lafferty JD (2006) Proceedings of the Twenty-Third International Conference, eds WW Cohen and A Moore (ACM, Pittsburgh), pp 113-120.
13. Airoldi EM, Blei DM, Fienberg SE, Xing EP (2008) Mixed membership stochastic blockmodels. J Mach Learn Res 9:1981-2014.
14. Rosen-Zvi M, Chemudugunta C, Griffiths TL, Smyth P, Steyvers M (2010) Learning author-topic models from text corpora. ACM T Inform Syst 28:1-38.
15. Griffiths TL, SteyversM, Tenenbaum J (2007) Topics in semantic representation. Psychol Rev 114:211-244.
16. Escobar M, West M (1995) Bayesian density estimation and inference using mixtures. J Am Stat Assoc 90:577-588.
17. Griffiths TL, Ghahramani Z (2005) Infinite latent feature models and the Indian buffet process. (University College, London), Technical Report GCNU-TR 2005-001.
18. Griffin J, Steele M (2006) Order-based dependent Dirichlet processes. J Am Stat Assoc 101:179-194.
19. Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101:1566-1581.
20. Duan JA, Guindani M, Gelfand AE (2007) Generalized spatial Dirichlet process models. Biometrika 94:809-825.