A Greedy Algorithm for Sparse Precision Matrix Approximation
Year: 2021
Author: Didi Lv, Xiaoqun Zhang
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 5 : pp. 693–707
Abstract
Precision matrix estimation is an important problem in statistical data analysis. This paper proposes a sparse precision matrix estimation approach, based on CLIME estimator and an efficient algorithm GISSρ that was originally proposed for l1 sparse signal recovery in compressed sensing. The asymptotic convergence rate for sparse precision matrix estimation is analyzed with respect to the new stopping criteria of the proposed GISSρ algorithm. Finally, numerical comparison of GISSρ with other sparse recovery algorithms, such as ADMM and HTP in three settings of precision matrix estimation is provided and the numerical results show the advantages of the proposed algorithm.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2005-m2019-0151
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 5 : pp. 693–707
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: Precision matrix estimation CLIME estimator Sparse recovery Inverse scale space method Greedy methods.
Author Details
Didi Lv Email
Xiaoqun Zhang Email