# Learning Gaussian Graphical Models Using Discriminated Hub Graphical Lasso

arXiv: https://arxiv.org/abs/1705.06364 · HTML: https://mlsanity.com/paper/1705.06364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 1 of 1)

- [Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm](https://mlsanity.com/paper/2308.08852/) (2023)

## Abstract (arXiv)

We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are known, we can always estimate the precision matrix better via DHGL than HGL. When no hubs are known, we use Graphical Lasso (GL) to provide information of hubs and find that the performance of DHGL will always be better than HGL if correct prior information is given and will seldom degenerate when the prior information is wrong.