Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2016-01-01
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| Series: | Computational and Mathematical Methods in Medicine |
| Online Access: | http://dx.doi.org/10.1155/2016/2078214 |