English(EN) DK-GBMKKM: Dynamic Kernel-Space Granular-Ball Multiple Kernel $k$-Means Clustering

新的DK-GBMKKM算法使聚类适应不断演变的核几何形状

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为DK-GBMKKM的新聚类算法，该算法在融合核空间内动态生成粒状球。与先前在输入空间一次生成球的方法不同，这种方法允许表示适应多核学习过程中不断演变的核几何形状。该算法还结合了样本大小加权的粒状球核，以维持不同大小球的贡献。在12个数据集上的实验表明，DK-GBMKKM取得了强大的聚类性能，并且代码已开源。 AI

影响引入了一种新颖的聚类算法，该算法可适应不断演变的数据几何形状，从而可能提高机器学习任务的性能。

排序理由这是一篇详细介绍新颖算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiaoyu Lian, Yuchao Zhang, Shuyin Xia, Siqi Zhong, Xuzhao Xiang · 2026-09-02 04:00

DK-GBMKKM: 动态内核空间粒度球多核 $k$-均值聚类

arXiv:2609.00647v1 Announce Type: new Abstract: Multiple kernel $k$-means integrates complementary nonlinear similarities by learning a combination of base kernels. Its pointwise optimization, however, is sensitive to noisy and boundary samples and repeatedly operates on sample-s…

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报道来源 [1]

DK-GBMKKM: 动态内核空间粒度球多核 $k$-均值聚类

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