Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-23
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized.
A common choice is convex l1 plus nuclear norm to regularize the searching process.
However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low.
In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition.
We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well.
We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness.
In addition, we extend our method to also allow the decomposition of fused structure-sparsity plus low rankness, providing a powerful tool for models with temporal information.
Specifically, we develop a nontrivial modified alternating direction method of multipliers with at least local convergence.
Finally, we use both synthetic and real data to validate the excellence of our method.
In the application of reconstructing two-stage cancer networks, “the Warburg effect” can be revealed directly.
American Psychological Association (APA)
Wang, Yanbo& Liu, Quan& Yuan, Bo. 2016. Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1100074
Modern Language Association (MLA)
Wang, Yanbo…[et al.]. Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1100074
American Medical Association (AMA)
Wang, Yanbo& Liu, Quan& Yuan, Bo. Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1100074
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100074