 | This is the item page for this Wikibase entity, intended for internal use and editing purposes. Please use this page instead for the normal view: JGL |
Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | JGL |
Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes |
Statements
19 December 2023
0 references
The Joint Graphical Lasso is a generalized method for estimating Gaussian graphical models/ sparse inverse covariance matrices/ biological networks on multiple classes of data. We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications. Reference: Danaher P, Wang P, Witten DM. (2013) <doi:10.1111/rssb.12033>.
0 references
Identifiers
20 January 2024
0 references
