# Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data

arXiv: https://arxiv.org/abs/1403.0904 · HTML: https://mlsanity.com/paper/1403.0904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2014.

## Built on by (most cited first, 2 of 2)

- [Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator](https://mlsanity.com/paper/2401.17760/) (2024)
- [Distributed Networked Multi-task Learning](https://mlsanity.com/paper/2410.03403/) (2024)

## Abstract (arXiv)

We study ridge estimation of the precision matrix in the high-dimensional setting where the number of variables is large relative to the sample size. We first review two archetypal ridge estimators and note that their utilized penalties do not coincide with common ridge penalties. Subsequently, starting from a common ridge penalty, analytic expressions are derived for two alternative ridge estimators of the precision matrix. The alternative estimators are compared to the archetypes with regard to eigenvalue shrinkage and risk. The alternatives are also compared to the graphical lasso within the context of graphical modeling. The comparisons may give reason to prefer the proposed alternative estimators.