In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this work, we address this limitation by providing an approach for inferring latent graphs that capture the intrinsic data relationships. By leveraging graph-based representations, our approach facilitates the seamless propagation of information throughout the graph, enabling the effective incorporation of global and local knowledge. Through evaluations on biomedical tabular datasets, we compare the capabilities of our approach to other contemporary methods. Our work demonstrates the significance of inter-instance relationship discovery as practical means for constructing robust latent graphs to enhance semi-supervised learning techniques. Our method achieves state-of-the-art results on three biomedical datasets.

翻译：在半监督学习领域，现有方法未能充分挖掘（未）标注数据间实例关系利用的潜力。为弥补这一局限，我们提出了一种推断潜在图的方法，以捕捉数据的内在关联。通过利用基于图的表示方法，我们的方法促进了信息在图上的无缝传播，从而有效整合全局与局部知识。在生物医学表格数据集上的评估中，我们将该方法的能力与其他当代方法进行了比较。本研究表明，实例关系发现作为构建鲁棒潜在图以增强半监督学习技术的实用手段具有重要意义。我们的方法在三个生物医学数据集上取得了最先进的结果。