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English(EN) Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDS

新的Max-D-SW距离增强了用于模式识别的多维缩放

作者 PulseAugur 编辑部 · [2 个来源] ·

本文介绍了一种Max-D-SW，它是Max-Sliced Wasserstein距离的调整版本，旨在改进用于模式识别的多维缩放（MDS）。Max-D-SW在标准正交基上聚合贡献，与原始公式相比具有数值优势，尤其是在处理重尾分布时。该研究还建立了样本复杂度界限，表明Max-D-SW在统计上是可处理的，并且改进的样本复杂度并不总是能保证更好的MDS性能。 AI

影响 引入了一种可能改进机器学习应用中模式识别的新度量。

排序理由 详细介绍了一种新的统计方法及其应用的学术论文。

在 arXiv stat.ML 阅读 →

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新的Max-D-SW距离增强了用于模式识别的多维缩放

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Flor Martinez-Sermeno, Arturo Jaramillo, Johan Van Horebeek ·

    经验分布与真实分布的桥接的调整沃瑟斯坦距离及其在MDS中的应用

    arXiv:2606.29665v1 Announce Type: new Abstract: This paper examines how metric adjustments to Multidimensional Scaling (MDS) can enhance its effectiveness as a visual tool for pattern recognition. The distance under consideration, referred to as Max-D-SW, is an adjustment of the …

  2. arXiv stat.ML TIER_1 English(EN) · Johan Van Horebeek ·

    经验分布与真实分布的桥接的调整沃瑟斯坦距离及其在MDS中的应用

    This paper examines how metric adjustments to Multidimensional Scaling (MDS) can enhance its effectiveness as a visual tool for pattern recognition. The distance under consideration, referred to as Max-D-SW, is an adjustment of the Max-Sliced Wasserstein distance. In contrast to …

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