Work

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.

Year: 2012

Type: article

Abstract: λ. This characterizes a very interesting property of a path of graphical lasso solutions. Furthermore, this simple rule, when used as a wrapper around existing algorithms for the graphical lasso, leads to enormous performance gains. For a range of values of λ, our proposal splits a large graphical lasso problem into smaller tractable problems, making it possible to solve an otherwise infeasible large-scale problem. We illustrate the graceful scalability of our proposal via synthetic and real-life microarray examples.

Source: PubMed

Authors: Rahul Mazumder, Trevor Hastie

Institution: Stanford University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of Biomedical Imaging and Bioengineering

Award: New Statistical Methods for Medical Signals and Images

OpenAlex ID: W2106802398

FWCI: 12.68

Cites: 3

Cited by: 106

Related to: 10