arXiv：cs.LG· Li Yuan, Yaxin Hou, Jiawei Tang, Yongbiao Gao, Yuheng Jia·· 4 小时前AI 评分27
双失配半监督学习的新方法：Hub for Outliers, Spokes for Inliers 均匀潜空间构建
Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning
AI 导读
针对半监督学习中标注与未标注数据在类别分布和标签空间双重失配的问题，研究者提出一种 hub-spoke 潜空间几何结构：已知类均匀分布在中心 hub 周围并围绕各自原型形成紧凑聚类，hub 则为高不确定性未知类样本提供低证据区域锚点，配合基于证据的分类器缓解多数类主导。实验显示该方法在多种设置下优于现有最优方法，最大提升 3.25%。
正文
Abstract:Semi-supervised learning typically assumes that labeled and unlabeled data share an identical class distribution and label space. However, this setting is often violated: unlabeled data may be imbalanced and contain unknown class samples, causing mismatches in both class distribution and label space. Such dual mismatch leads to majority classes dominating the latent space and unknown class samples being overconfidently misclassified, degrading feature discriminability and pseudo-label quality. To address this, we propose a hub-spoke latent geometry, where known classes are uniformly distributed around a central hub and each class forms compact clusters around its prototype, while the hub provides an anchor for a low-evidence region specifically designed for high-uncertainty unknown class samples. Integrated with an evidence-based classifier, this geometry ultimately enhances feature discriminability and uncertainty separation by mitigating majority-class domination through structured feature organization and guiding high-uncertainty unknown class samples toward the hub. Extensive experiments show that our method outperforms state-of-the-art methods, with a maximum improvement of 3.25% across various settings.
| Comments: | Equal contribution by Li Yuan and Yaxin Hou. Corresponding author: Yuheng Jia. Emails: {yuan-li,yaxin,230259148,yhjia}@seu.this http URL, gaoyb@qlu.this http URL. 18 pages |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07610 [cs.LG] |
| (or arXiv:2610.07610v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07610 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Li Yuan [view email]
[v1]
Tue, 6 Oct 2026 01:55:51 UTC (1,528 KB)
来源：arXiv：cs.LG · arxiv.org