English(EN) Non-Parametric Mutual Information Estimation with Vine Copulas

新的VINE估计器提供高效的互信息计算

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了VINE，一种利用简化D-vine copulas的新型非参数互信息估计器。该方法将密度比简化为成对copulas的乘积，从而实现高效灵活的估计。与最先进的神经网络MI估计器相比，VINE在低维到中等维度的数据集上表现出具有竞争力的准确性，同时需要更少的计算资源。 AI

影响这项研究可能导致更有效的方法来分析机器学习中的复杂数据集。

排序理由该集群包含一篇详细介绍互信息估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Sigurd Holmsen, Berit Omli {\O}ksnes, Ingrid Hob{\ae}k Haff, Sylvia Richardson, Ali Ramezani-Kebrya · 2026-11-06 05:00

基于藤蔓联结的非参数互信息估计

arXiv:2611.07693v1 Announce Type: new Abstract: We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas. Approximating the joint density with a simplified D-vine allows the density ratio to simplify to a product of pair copulas, wh…