The joint graphical lasso for inverse covariance estimation across multiple classes

Understand

We consider the problem of estimating multiple related but distinct graphical models on the basis of a high-dimensional data set with observations that belong to distinct classes.

• A motivating example occurs in the analysis of gene expression data for tissue samples with and without cancer.
• In this case, we might wish to estimate a gene expression network for the normal tissue and a gene expression network for the tumor tissue.
• We expect the two gene expression networks to be similar but not identical to each other, and so more accurate estimation of these two networks may be possible using a joint approach.