Work

Node-Based Learning of Multiple Gaussian Graphical Models

Year: 2013

Type: article

Abstract: penalty function, we formulate two convex optimization problems that correspond to these two assumptions. We solve these problems using an alternating direction method of multipliers algorithm, and we derive a set of necessary and sufficient conditions that allows us to decompose the problem into independent subproblems so that our algorithm can be scaled to high-dimensional settings. Our proposal is illustrated on synthetic data, a webpage data set, and a brain cancer gene expression data set.

Source: PubMed

DOI: 10.48550/arxiv.1303.5145

Authors: Mohan, Karthik, London, Palma, Fazel, Maryam, Witten, Daniela, Lee, Su-In

Institution: University of Washington

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Division of Electrical, Communications and Cyber Systems

Awards: CAREER: Parsimonious Modeling via Matrix Minimization, High-dimensional unsupervised learning, with applications to genomics, G6250954628, CAREER: Flexible Network Estimation from High-Dimensional Data