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Projected Subgradient Methods for Learning Sparse Gaussians

Date

Authors

Duchi, John

Gould, Stephen

Koller, Daphne

Publisher

AUAI Press

Abstract

Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the ℓ1-norm as a regularization on the inverse covariance mat

Keywords

Keywords: Asymptotic complexity; Gaussian Markov random field; Gaussians; Generalization performance; High dimensional spaces; Inverse covariance; matrix; Projected gradient; Shape modeling; Subgradient methods; Artificial intelligence; Gaussian noise (electronic);

URI

http://hdl.handle.net/1885/58479

Collections

ANU Research Publications

Source

Proceedings of the Twenty Fourth Conference on Uncertainty in Artificial Intelligence

Type

Conference paper

DOI

10.1.1.143.8948

Restricted until

2037-12-31

Downloads

File

Description

01_Duchi_Projected_Subgradient_Methods_2008.pdf (183.27 KB)

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