English(EN) Recovery thresholds for hidden weighted sparse graphs

新方法表征隐藏加权稀疏图的恢复阈值

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-12 10:44

研究人员开发了一种统一的表征方法，用于刻画噪声高维数据中隐藏结构恢复的信息论极限。该研究侧重于识别嵌入在随机加权完全图中的未知图，其中边权重遵循特定分布。研究结果将KL散度与Erdős-Rényi随机图模型的阈值联系起来，并证明了对于高斯等特定分布存在全有或全无现象。 AI

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的统计推断数学方法。[lever_c_demoted from research: ic=2 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Zhe Hou, Jingcheng Liu · 2026-06-15 04:00

隐藏加权稀疏图的恢复阈值

arXiv:2606.14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference. We investigate the recovery thresholds for a graph hidden in a randomly weighted complete graph. Specifically, an u…
arXiv cs.LG TIER_1 English(EN) · Jingcheng Liu · 2026-06-12 10:44

隐藏加权稀疏图的恢复阈值

Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference. We investigate the recovery thresholds for a graph hidden in a randomly weighted complete graph. Specifically, an unknown graph $H^* \in H_n$ is chosen uniformly at …