Low-rank Matrix Sensing With Dithered One-Bit Quantization
Information Theory
2024-01-31 v2 math.IT
Abstract
We explore the impact of coarse quantization on low-rank matrix sensing in the extreme scenario of dithered one-bit sampling, where the high-resolution measurements are compared with random time-varying threshold levels. To recover the low-rank matrix of interest from the highly-quantized collected data, we offer an enhanced randomized Kaczmarz algorithm that efficiently solves the emerging highly-overdetermined feasibility problem. Additionally, we provide theoretical guarantees in terms of the convergence and sample size requirements. Our numerical results demonstrate the effectiveness of the proposed methodology.
Cite
@article{arxiv.2309.04045,
title = {Low-rank Matrix Sensing With Dithered One-Bit Quantization},
author = {Farhang Yeganegi and Arian Eamaz and Mojtaba Soltanalian},
journal= {arXiv preprint arXiv:2309.04045},
year = {2024}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2308.00695