@inproceedings{c5fdad913c8143c6b6ec33969fbb375f,

title = "C-ISTA: Iterative Shrinkage-Thresholding Algorithm for Sparse Covariance Matrix Estimation",

abstract = "Covariance matrix estimation is a fundamental task in many fields related to data analysis. As the dimension of the covariance matrix becomes large, it is desirable to obtain a sparse estimator and an efficient algorithm to compute it. In this paper, we consider the covariance matrix estimation problem by minimizing a Gaussian negative log-likelihood loss function with an ℓ1 penalty, which is a constrained non-convex optimization problem. We propose to solve the covariance estimator via a simple iterative shrinkage-thresholding algorithm (C-ISTA) with provable convergence. Numerical simulations with comparison to the benchmark methods demonstrate the computational efficiency and good estimation performance of C-ISTA.",

author = "Wenfu Xia and Ziping Zhao and Ying Sun",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",

year = "2023",

doi = "10.1109/SSP53291.2023.10207953",

language = "English (US)",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

pages = "215--219",

booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",

address = "United States",

}