研究人员利用费马距离（一种对数据密度和聚类假设敏感的度量）开发了高维半监督分类的新方法。提出的加权k近邻和多维标度诱导分类器旨在提高标记数据稀缺但未标记数据充足时的性能。理论分析表明，具有真实费马距离的加权k-NN分类器是minimax最优的，实验表明其结果与现有的基于图的方法相比具有竞争力。 AI
影响 为提高高维空间中的半监督学习引入了新颖的理论框架和实用方法。
排序理由 在arXiv上发表的学术论文，详细介绍了一种新的分类方法。
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arXiv:2604.23573v1 Announce Type: new Abstract: Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We address this challenge under high-dimensional data by leveraging the manifold and clust…
Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We address this challenge under high-dimensional data by leveraging the manifold and cluster assumptions. Based on the Fermat distance, a …