In recent years, domain adaptation and transfer learning are known as promising techniques with admirable performance to deal with problems with distribution difference between the training (source domain) and test (target domain) data. In this paper, a novel unsupervised multi-source transductive transfer learning approach, referred to as multi-source domain adaptation for image classification (MDA), is proposed, to transfer knowledge across the selected samples of multiple-source domains and samples of target domain into a shared low-dimensional subspace with maximum decision regions. MDA extends maximum mean discrepancy criteria across multiple-source domains to find an optimal projection subspace and constructs embedded condensed domain-invariant clusters. Furthermore, MDA minimizes empirical risk and maximizes the rate of consistency between manifold and prediction function via learning an optimal classification. Extensive evaluations on two types of visual benchmark datasets under different difficulties illustrate that MDA significantly outperforms other baseline and state-of-the-art methods in both multiple- and single-source tasks. Our source code is available at https://github.com/jtahmores/MDA.

中文翻译：

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多源领域自适应的图像分类

近年来，领域适应和转移学习被称为具有令人称赞的性能的有前途的技术，用于解决训练（源域）和测试（目标域）数据之间的分布差异问题。本文提出了一种新颖的无监督多源转导迁移学习方法，称为多源域图像分类适应（MDA），可在选定的多源域样本和目标域样本之间传递知识进入具有最大决策区域的共享低维子空间。MDA扩展了跨多个源域的最大平均差异标准，以找到最佳投影子空间，并构造了嵌入式的凝聚域不变聚类。此外，MDA通过学习最佳分类，将经验风险降到最低，并使流形和预测函数之间的一致性率最大化。在不同困难下对两种类型的可视基准数据集进行的广泛评估表明，在多源任务和单源任务中，MDA的性能均明显优于其他基准和最新方法。我们的源代码可从https://github.com/jtahmores/MDA获得。