Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping

Fuente: arXiv
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Main Authors: Maly, Johannes, Veselovska, Anna
Format: Preprint
Published: 2026
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author Maly, Johannes
Veselovska, Anna
author_facet Maly, Johannes
Veselovska, Anna
contents Graph data are ubiquitous in natural sciences and machine learning. In this paper, we consider the problem of quantizing graph structured, bandlimited data to few bits per entry while preserving its information under low-pass filtering. We propose an efficient single-shot noise shaping method that achieves state-of-the-art performance and comes with rigorous error bounds. In contrast to existing methods it allows reliable quantization to arbitrary bit-levels including the extreme case of using a single bit per data coefficient.
format Preprint
id arxiv_https___arxiv_org_abs_2602_08669
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping
Maly, Johannes
Veselovska, Anna
Information Theory
Graph data are ubiquitous in natural sciences and machine learning. In this paper, we consider the problem of quantizing graph structured, bandlimited data to few bits per entry while preserving its information under low-pass filtering. We propose an efficient single-shot noise shaping method that achieves state-of-the-art performance and comes with rigorous error bounds. In contrast to existing methods it allows reliable quantization to arbitrary bit-levels including the extreme case of using a single bit per data coefficient.
title Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping
topic Information Theory
url https://arxiv.org/abs/2602.08669