Learning sparse gaussian Markov networks using a greedy coordinate ascent approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 196-212

  Scheinberg, Katya ^a   Rish, Irina ^b  

^a Columbia University ^*  (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMMONLY USED; COMPUTATIONAL TIME; COORDINATE ASCENT; FALSE POSITIVE; GAUSSIANS; GREEDY ALGORITHMS; INVERSE COVARIANCE; MARKOV NETWORKS; NETWORK LINKS; NETWORK STRUCTURES; NEURO-IMAGING; REAL LIFE DATA; RECONSTRUCTION ERROR; RECOVERY PROPERTIES; REGULARIZATION PARAMETERS; SELECTION PROBLEMS; SIMULATED NETWORKS;

LEARNING SYSTEMS;

EID: 77958041597     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_13     Document Type: Conference Paper

References (21)

1. Banerjee, O., El Ghaoui, L., d'Aspremont, A.: Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Learning Research 9, 485-516 (2008)
2. Barabasi,A.-L.,Albert, R.: Emergenceofscaling in random networks. Science286, 509-512 (1999)
3. Buhl, S.L.: On the existence ofmaximum likelihood estimators for graphical gaussian mod-els. Scandinavian Journal of Statistics 20(3), 263-270 (1993)
4. Dempster, A.P.: Covariance selection. Biometrics 28(1), 157-175 (1972)
5. Duchi, J., Gould, S., Koller, D.: Projected subgradient methods for learning sparse gaussians. In: Proc. of UAI-08 (2008)
6. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Statist. 32(1), 407-499 (2004)
7. Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graph-ical lasso. Biostatistics (2007)
8. Lauritzen, S.: Graphical Models. Oxford University Press, Oxford (1996)
9. Lu, Z.: Smooth optimization approach for sparse covariance selection. SIAM Journal on Optimization 19(4), 1807-1827 (2009)
10. Marlin, B., Murphy, K.: Sparse gaussian graphical models with unknown block structure. In: Proc. of ICML-09 (2009)
11. Meinshausen, N., Buhlmann, P.: High dimensional graphs and variable selection with the Lasso. Annals of Statistics 34(3), 1436-1462 (2006)
12. Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to decode cognitive states from brain images. Machine Learning 57, 145-175 (2004)
13. Ravikumar, P., Wainwright, M., Raskutti, G., Yu, B.: Model selection in Gaussian graphical models: High-dimensional consistency ofl1-regularized MLE. In: NIPS-08 (2008)
14. Rothman, A., Bickel, P., Levina, E., Zhu, J.: Sparse permutation invariant covariance estima-tion. Electronic Journal of Statistics (2), 494-515 (2008)
15. Schmidt, M., van den Berg, E., Friedlander, M., Murphy, K.: Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm. In: Proc. of AISTATS-09 (2009)
16. Stolovitzky, G., Prill, R.J., Califano, A.: Lessons from the dream2 challenges. Annals of the New YorkAcademy ofSciences (1158), 159-195 (2009)
17. Tibshirani, R.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statis-tical Society, Series B 58(1), 267-288 (1996)
18. Wainwright, M., Ravikumar, P., Lafferty, J.: High-Dimensional Graphical Model Selection Using Regularized Logistic Regression. In: NIPS 19, pp. 1465-1472 (2007)
19. Wen, Z., Goldfarb, D., Ma, S., Scheinberg, K.: Row by row methods for semidefinite pro-gramming. Technical report, Department of IEOR, Columbia University (2009)
20. Wolkowicz, H., Saigal, R., Vanenberghe, L. (eds.): Handbook of Semidefinite Programming. Kluwer Academic Publishers, Dordrecht (2000)
21. Yuan, M., Lin, Y.: Model Selection and Estimation in the Gaussian Graphical Model. Biometrika94(1), 19-35 (2007)