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English(EN) Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms

Gromov-Wasserstein 量化扩展 K-Means 以实现几何感知聚类

作者 PulseAugur 编辑部 · [1 个来源] ·

本文将 Gromov-Wasserstein (GW) 量化作为传统 k-means 聚类的扩展。与在空间内进行点聚类的标准 Wasserstein 量化不同，GW 量化还考虑了空间本身的几何结构。该研究证明了 GW 量化的解的存在性，并提出了一种类似于 Lloyd 算法的数值逼近算法。研究还分析了欧几里得几何的量化速率，并通过实验证明 GW 量化提供了新的建模能力，例如用于 3D 形状和神经网络剪枝。 AI

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Gromov-Wasserstein 量化扩展 K-Means 以实现几何感知聚类

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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Gromov-Wasserstein 量化与聚类：结构、速率与算法

    Clustering is a fundamental class of data analysis techniques with the most important representatives being centroid-based methods like $k$-means. Such methods are strongly connected to quantization problems, which aim to approximate general probability measures with discrete one…

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