Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping
It addresses the problem of efficient graph data compression for signal processing and machine learning applications, offering a practical solution with theoretical guarantees.
The paper proposes a single-shot noise shaping method for quantizing bandlimited graph data to few bits per entry, achieving state-of-the-art performance with rigorous error bounds, including reliable 1-bit quantization.
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.