论文标题
标签传播和弱监督
Label Propagation with Weak Supervision
论文作者
论文摘要
半监督学习和弱监督学习是重要的范式，旨在减少当前机器学习应用中对标记数据的不断增长的需求。在本文中，我们介绍了经典标签传播算法（LPA）（Zhu＆Ghahramani，2002）的新分析，该算法还利用了有用的先前信息，特别是对未标记数据的概率假设的标签。我们提供了一个错误结合，该错误限制了基础图的局部几何特性和先前信息的质量。我们还提出了一个框架，以结合多种嘈杂信息来源。特别是，我们考虑了弱监督的设置，我们的信息来源是弱标签。我们证明了我们在多个基准弱监督分类任务上的方法的能力，从而在现有的半监督和弱监督的方法上显示了改进。
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that moreover takes advantage of useful prior information, specifically probabilistic hypothesized labels on the unlabeled data. We provide an error bound that exploits both the local geometric properties of the underlying graph and the quality of the prior information. We also propose a framework to incorporate multiple sources of noisy information. In particular, we consider the setting of weak supervision, where our sources of information are weak labelers. We demonstrate the ability of our approach on multiple benchmark weakly supervised classification tasks, showing improvements upon existing semi-supervised and weakly supervised methods.
