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：图数据在自然科学与机器学习中无处不在。本文研究如何将图结构化的带限数据量化为每项仅占数比特，同时使其在低通滤波下保持信息完整性。我们提出一种高效的单次噪声成形方法，该方法实现了最先进的性能并具备严格的误差界。与现有方法相比，本方法支持可靠地量化至任意比特深度，包括对每个数据系数仅使用单比特的极端情况。