English(EN) Orthogonal Procrustes problem preserves correlations in synthetic data

新方法恢复合成数据中的相关性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-10 04:00

研究人员开发了一种新的合成表格数据后处理技术，该技术使用正交普罗克拉斯问题来恢复原始数据的皮尔逊相关结构。该方法旨在保留依赖结构，这对于涉及隐私、数据共享和稀缺性的应用至关重要。实验表明，该方法在保持个体特征分布、数据几何形状和下游分类任务性能的同时，有效地恢复了相关性。 AI

影响通过保留合成数据的统计特性来增强其效用，可能改进隐私保护的AI开发。

排序理由这是一篇详细介绍合成数据生成新方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Oussama Ounissi, Nicklas J\"averg\r{a}rd, Assaad Zeghina, Adrian Muntean · 2026-06-10 04:00

正交普罗克拉斯特定理保留合成数据中的相关性

arXiv:2510.02405v2 Announce Type: replace-cross Abstract: Synthetic data generation is increasingly used in applications involving privacy preservation, data sharing, and data scarcity. In many situations, preserving the dependence structure of the original data is of central int…