Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918486589046784 |
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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 |