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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 ℓ (1) plus nuclear norm to regularize the searching process. However, the best estimator performance is not alwa...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi Publishing Corporation
2016
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097857/ https://www.ncbi.nlm.nih.gov/pubmed/27843485 http://dx.doi.org/10.1155/2016/2078214 |