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Main Authors: Huang, Gao, Li, Song
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2407.03175
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author Huang, Gao
Li, Song
author_facet Huang, Gao
Li, Song
contents This note demonstrates that we can stably recover all symmetric Toeplitz matrices $\pmb{X}_0\in\mathbb{R}^{n\times n}$ of rank at most $r$ from a number of rank-one subgaussian measurements on the order of $r\log^{2} n$ with an exponentially decreasing failure probability by employing a nuclear norm minimization program. Our approach utilizes descent cone analysis through Mendelson's small ball method with the Toeplitz constraint. The key ingredient is to determine the spectral norm of a random matrix with Toeplitz structure, which may be of independent interest. This improves upon earlier analyses and resolves the conjecture in Chen et al. (IEEE Transactions on Information Theory, 61(7):4034--4059, 2015).
format Preprint
id arxiv_https___arxiv_org_abs_2407_03175
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix
Huang, Gao
Li, Song
Information Theory
This note demonstrates that we can stably recover all symmetric Toeplitz matrices $\pmb{X}_0\in\mathbb{R}^{n\times n}$ of rank at most $r$ from a number of rank-one subgaussian measurements on the order of $r\log^{2} n$ with an exponentially decreasing failure probability by employing a nuclear norm minimization program. Our approach utilizes descent cone analysis through Mendelson's small ball method with the Toeplitz constraint. The key ingredient is to determine the spectral norm of a random matrix with Toeplitz structure, which may be of independent interest. This improves upon earlier analyses and resolves the conjecture in Chen et al. (IEEE Transactions on Information Theory, 61(7):4034--4059, 2015).
title Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix
topic Information Theory
url https://arxiv.org/abs/2407.03175