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.

翻译：图数据在自然科学和机器学习中无处不在。本文考虑将图结构的带限数据量化到每项仅用少量比特，同时保证其在低通滤波下信息得以保留的问题。我们提出了一种高效的单次噪声整形方法，该方法实现了最优性能并具有严格的误差界。与现有方法不同，该方法允许可靠地量化到任意比特级别，包括每个数据系数仅使用单比特的极端情况。