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A Greedy Algorithm for Sparse Precision Matrix Approximation

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.

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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