English(EN) T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models

新的T-ARC聚类方法利用拓扑纠正k-means的几何偏差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员推出了一种新颖的拓扑感知随机聚类方法T-ARC，旨在克服传统k-means聚类固有的几何偏差。该新方法通过将数据的底层结构建模为潜在图，将拓扑信息直接整合到优化目标中。T-ARC结合了数据保真项和图割惩罚项，使用由持久同调（persistent homology）启发的随机块模型来捕获多尺度连通性。在合成和真实世界数据集（包括Fashion-MNIST）上的实验表明，T-ARC在恢复复杂拓扑结构方面表现优于k-means，并且比k-means更稳定。 AI

影响引入了一种新颖的聚类算法，可能改进机器学习应用中的数据分析。

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在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Serena Grazia De Benedictis, Andersen Ang, Nicoletta Del Buono, Flavia Esposito, Laura Selicato · 2026-10-02 04:00

T-ARC：通过分布鲁棒随机块模型实现拓扑感知随机聚类

arXiv:2609.39466v1 Announce Type: new Abstract: In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by embedding topological information directly into the optimization objective. Buil…

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T-ARC：通过分布鲁棒随机块模型实现拓扑感知随机聚类

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