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English(EN) K-Means as the Zero-Temperature Limit of Normalized RBF Assignments: Rates, Trajectories, and Exact Recovery with Entmax

新研究将 K-Means 与归一化 RBF 分配联系起来

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了 K-Means 聚类与归一化径向基函数 (RBF) 分配之间的关系，提出 K-Means 可以被视为这些分配的零温度极限。该研究详细介绍了如何通过香农熵惩罚放松 K-Means 分配，从而得到归一化高斯 RBF 责任和收敛于 K-Means 畸变的降低的目标函数。论文还证明了交替最小化作为该目标上的预条件梯度方法，其硬极限是 Lloyd 算法，并引入了 Entmax-1.5 作为在特定条件下恢复硬分配和 Lloyd 更新的方法。 AI

影响 这项研究在基本聚类算法和可微分学习方法之间提供了理论联系，可能为无监督学习和表示学习带来新方法。

排序理由 该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新研究将 K-Means 与归一化 RBF 分配联系起来

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该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Felipe de Jesus Felix Arredondo, Manuel Alejandro Ucan Puc, Carlos Astengo Nouguez ·

    K-Means as the Zero-Temperature Limit of Normalized RBF Assignments: Rates, Trajectories, and Exact Recovery with Entmax

    arXiv:2603.04625v2 Announce Type: replace Abstract: K-Means is usually treated as a discrete procedure outside gradient-based learning, whereas normalized radial basis function (RBF) layers are smooth. We make precise the sense in which K-Means is the zero-temperature limit of su…

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