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Reliable One-Bit Quantization of Bandlimited Graph Data via Single-Shot Noise Shaping

MCML Authors

Johannes Maly

Prof. Dr.

Associate

Mathematical Data Science and Artificial Intelligence

Anna Veselovska

Dr.

→ Group Massimo Fornasier
Applied Numerical Analysis

Abstract

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.

misc MV26