This article conducts a large dimensional study of a simple yet quite versatile classification model, encompassing at once multi-task and semi-supervised learning, and taking into account uncertain labeling. Using tools from random matrix theory, we characterize the asymptotics of some key functionals, which allows us on the one hand to predict the performances of the algorithm, and on the other hand to reveal some counter-intuitive guidance on how to use it efficiently. The model, powerful enough to provide good performance guarantees, is also straightforward enough to provide strong insights into its behavior.

翻译：本文对一种简单但功能多样的分类模型进行了大维度研究，该模型同时涵盖多任务学习和半监督学习，并考虑了标签不确定性。利用随机矩阵理论工具，我们刻画了一些关键泛函的渐近特性，这一方面使我们能够预测算法的性能，另一方面揭示了如何高效使用该算法的一些反直觉指导。该模型既足够强大以提供良好的性能保证，又足够直观以深入洞察其行为。