Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications

As major components of the random matrix theory, Gaussian random matrices have been playing an important role in many fields, because they are both unitary invariant and have independent entries and can be used as models for multivariate data or multivariate phenomena. Tail bounds for eigenvalues of...

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Main Authors: Xianjie Gao, Mingliang Zhang, Jinming Luo
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2022/1456713
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author Xianjie Gao
Mingliang Zhang
Jinming Luo
author_facet Xianjie Gao
Mingliang Zhang
Jinming Luo
author_sort Xianjie Gao
collection DOAJ
description As major components of the random matrix theory, Gaussian random matrices have been playing an important role in many fields, because they are both unitary invariant and have independent entries and can be used as models for multivariate data or multivariate phenomena. Tail bounds for eigenvalues of Gaussian random matrices are one of the hot study problems. In this paper, we present tail and expectation bounds for the ℓ1 norm of Gaussian random matrices, respectively. Moreover, the tail and expectation bounds for the ℓ1 norm of the Gaussian Wigner matrix are calculated based on the resulting bounds. Compared with existing results, our results are more suitable for the high-dimensional matrix case. Finally, we study the tail bounds for the parameter vector of some existing regularization algorithms.
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institution Kabale University
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publishDate 2022-01-01
publisher Wiley
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series Journal of Mathematics
spelling doaj-art-cd124a87d0cc45b9afb3147e4320e03b2025-02-03T06:12:24ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/1456713Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with ApplicationsXianjie Gao0Mingliang Zhang1Jinming Luo2Department of Basic SciencesSchool of Mathematics and StatisticsSchool of Mathematical SciencesAs major components of the random matrix theory, Gaussian random matrices have been playing an important role in many fields, because they are both unitary invariant and have independent entries and can be used as models for multivariate data or multivariate phenomena. Tail bounds for eigenvalues of Gaussian random matrices are one of the hot study problems. In this paper, we present tail and expectation bounds for the ℓ1 norm of Gaussian random matrices, respectively. Moreover, the tail and expectation bounds for the ℓ1 norm of the Gaussian Wigner matrix are calculated based on the resulting bounds. Compared with existing results, our results are more suitable for the high-dimensional matrix case. Finally, we study the tail bounds for the parameter vector of some existing regularization algorithms.http://dx.doi.org/10.1155/2022/1456713
spellingShingle Xianjie Gao
Mingliang Zhang
Jinming Luo
Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
Journal of Mathematics
title Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
title_full Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
title_fullStr Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
title_full_unstemmed Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
title_short Tail Bounds for ℓ1 Norm of Gaussian Random Matrices with Applications
title_sort tail bounds for l1 norm of gaussian random matrices with applications
url http://dx.doi.org/10.1155/2022/1456713
work_keys_str_mv AT xianjiegao tailboundsforl1normofgaussianrandommatriceswithapplications
AT mingliangzhang tailboundsforl1normofgaussianrandommatriceswithapplications
AT jinmingluo tailboundsforl1normofgaussianrandommatriceswithapplications