用图上无穷拉普拉斯改进半监督学习，解决标签不平衡问题。
Graph-Based Semi-Supervised Segregated Lipschitz Learning
- 基于图结构与无穷拉普拉斯理论传播标签
- 在少量标注数据下准确率优于现有方法
- 适合处理类别不均衡的半监督场景
本文提出一种基于图的半监督学习方法，利用无穷拉普拉斯算子在仅少数样本有标签的情况下进行标签传播。通过将空间分离理论从拉普拉斯算子扩展到无穷拉普拉斯算子（连续与离散情形），该方法有效应对机器学习中常见的类别不平衡问题。在多个基准数据集上的实验表明，本方法不仅分类准确率高于现有方法，且在标注数据有限时仍能实现高效标签传播。
原文摘要 · Abstract (English)
This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled. By extending the theory of spatial segregation from the Laplace operator to the infinity Laplace operator, both in continuum and discrete settings, our approach provides a robust method for dealing with class imbalance, a common challenge in machine learning. Experimental validation on several benchmark datasets demonstrates that our method not only improves classification accuracy compared to existing methods but also ensures efficient label propagation in scenarios with limited labeled data.
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