Low-rank Matrix Sensing With Dithered One-Bit Quantization

Farhang Yeganegi; Arian Eamaz; Mojtaba Soltanalian

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arXiv · 2309.04045

Low-rank Matrix Sensing With Dithered One-Bit Quantization

Farhang Yeganegi·Arian Eamaz·Mojtaba Soltanalian

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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.

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Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian. 2023-09-07. Low-rank Matrix Sensing With Dithered One-Bit Quantization. https://arxiv.org/abs/2309.04045

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