Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields-Reference-Cited by-同舟云学术

Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields

Published:2023-01-01 Issue:1 Volume:E106.A Page:23-34
ISSN:0916-8508
Container-title:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
language:en
Short-container-title:IEICE Trans. Fundamentals

Author:

KOYAKUMARU Tatsuya¹,YUKAWA Masahiro¹,PAVEZ Eduardo²,ORTEGA Antonio²

Affiliation:

1. Department of Electronics and Electrical Engineering, Keio University

2. Department of Electrical and Computer Engineering, University of Southern California

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Link

https://www.jstage.jst.go.jp/article/transfun/E106.A/1/E106.A_2021EAP1153/_pdf

Reference48 articles.

1. [1] H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications, 1st ed., 2005. 10.1201/9780203492024

2. [2] J. Friedman, T. Hastie, and R. Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,” Biostatistics, vol.9, no.3, pp.432-441, 2008. 10.1093/biostatistics/kxm045

3. [3] R. Mazumder and T. Hastie, “The graphical lasso: New insights and alternatives,” Electron. J. Statist., vol.6, pp.2125-2149, 2012. 10.1214/12-ejs740

4. [4] N. Meinshausen and P. Buhlmann, “High-dimensional graphs and variable selection with the lasso,” Ann. Statist., vol.34, no.3, pp.1436-1462, 2006. 10.1214/009053606000000281

5. [5] O. Banerjee, L.E. Ghaoui, and A. d'Aspremont, “Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data,” Journal of Machine Learning Research, vol.9, no.15, pp.485-516, 2008.

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