A constrained $$\ell $$1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models-Reference-Cited by-同舟云学术

A constrained $$\ell $$1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models

Published:2017-06-21 Issue:9-10 Volume:106 Page:1381-1417
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Wang Beilun,Singh Ritambhara,Qi Yanjun

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s10994-017-5635-7/fulltext.html

Reference67 articles.

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3. Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

4. Buchman, D., Schmidt, M., Mohamed, S., Poole, D., & de Freitas, N. (2012). On sparse, spectral and other parameterizations of binary probabilistic models. In AISTATS (pp. 173–181)

5. Cai, T., Liu, W., & Luo, X. (2011). A constrained 1 minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association, 106(494), 594–607.

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