Low-rank Matrix Sensing With Dithered One-Bit Quantization

Autor:	Yeganegi, Farhang, Eamaz, Arian, Soltanalian, Mojtaba
Rok vydání:	2023
Předmět:	Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:2308.00695
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.04045 Zobrazit plný text záznamu View this record from Arxiv