Domain generalization (DG) aims to incorporate knowledge from multiple source
domains into a single model that could generalize well on unseen target
domains. This problem is ubiquitous in practice since the distributions of the
target data may rarely be identical to those of the source data. In this paper,
we propose Multidomain Discriminant Analysis (MDA) to address DG of
classification tasks in general situations. MDA learns a domain-invariant
feature transformation that aims to achieve appealing properties, including a
minimal divergence among domains within each class, a maximal separability
among classes, and overall maximal compactness of all classes. Furthermore, we
provide the bounds on excess risk and generalization error by learning theory
analysis. Comprehensive experiments on synthetic and real benchmark datasets
demonstrate the effectiveness of MDA.

本文提出了一种多领域判别分析方法 (MDA) 来处理分类任务中的域泛化问题，通过学习一个域不变的特征变换，旨在实现每个类别之间领域的最小差异、类别之间最大可分性以及整体最大紧凑性，在学习理论分析中提供了过度风险和泛化误差的限制，通过对合成数据集和真实基准数据集的全面实验，证明了 MDA 的有效性。