用同步模型在单位超球面上聚类，更适配球面数据几何结构。
Synchronization-based clustering on the unit hypersphere
- 基于高维广义库朗莫托模型，模拟点间同步实现聚类
- 在合成与真实数据集上表现优于或接近传统方法
- 适合处理具有球面特性的数据，如文本、图像分类
在基因表达分析、文本和图像分类等多个领域中，单位超球面上的聚类是一个基础问题。传统聚类方法往往不适用于单位球面数据，因其未能考虑球面的几何结构。本文提出一种新算法，用于在单位球面 $\mathbf{S}^{d-1}$ 上进行聚类，其核心思想基于 $d$-维广义库朗莫托模型。通过在合成数据与真实数据集上的实验验证，结果表明该方法在聚类准确率方面与现有传统方法相当或更优。
原文摘要 · Abstract (English)
Clustering on the unit hypersphere is a fundamental problem in various fields, with applications ranging from gene expression analysis to text and image classification. Traditional clustering methods are not always suitable for unit sphere data, as they do not account for the geometric structure of the sphere. We introduce a novel algorithm for clustering data represented as points on the unit sphere $\mathbf{S}^{d-1}$. Our method is based on the $d$-dimensional generalized Kuramoto model. The effectiveness of the introduced method is demonstrated on synthetic and real-world datasets. Results are compared with some of the traditional clustering methods, showing that our method achieves similar or better results in terms of clustering accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。