Li Ding, Dick Scott
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB Canada T6G 1H9.
Complex Intell Systems. 2022;8(2):1561-1577. doi: 10.1007/s40747-021-00611-7. Epub 2022 Jan 4.
Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.
基于图的算法是半监督学习的有效方法。然而，将这些算法扩展到多标签分类情况的工作相对较少。我们推导出了流形正则化算法到多标签分类的扩展，它比一般的向量流形正则化方法要简单得多。然后，我们用一种加权策略增强我们的算法，以允许对具有真实标签与诱导标签的实例之间的模型产生不同的影响。在四个基准多标签数据集上的实验表明，在各种标签稀疏程度下，与现有的半监督多标签分类算法相比，所得算法总体表现更好。与最先进的监督多标签方法（当然是完全标注的）的比较也表明，即使有大量未标注示例，我们的算法也优于所有这些方法。
