Domain adaptation learning is proposed as an effective technology for leveraging rich supervision knowledge from the related domain(s) to learn a reliable classifier for a new domain. One popular kind of domain adaptation methods is based on feature representation. However, such methods fail to consider the within-class and between-class relations after obtaining the new representation. In addition, they do not consider the negative effects of features that might be redundant or irrelevant to the final classification. To this end, a novel domain-invariant feature learning method based on the maximum margin criterion and sparsity technique for unsupervised domain adaptation is proposed in this paper, referred to as discriminative and informative joint distribution adaptation (DIJDA). Specifically, DIJDA adopts the maximum margin criterion in the adaptation process such that the transformed samples are near to those in the same class but segregated from those in different classes. As a result, the discriminative knowledge referred from source labels can be transferred to target domain effectively. Moreover, DIJDA imposes a row-sparsity constraint on the transformation matrix, which enforces rows of the matrix corresponding to inessential feature attributes to be all zero. Therefore, the most informative feature attributes can be extracted. Compared with several state-of-the-art methods, DIJDA substantially improves the classification results on five widely used benchmark datasets, which demonstrates the effectiveness of the proposed method.

领域适应性学习被提议为一种有效的技术，可以利用来自相关领域的丰富监督知识来学习新领域的可靠分类器。一种流行的领域自适应方法是基于特征表示的。但是，这样的方法在获得新的表示之后无法考虑类内和类间关系。此外，他们没有考虑可能与最终分类无关或无关的要素的负面影响。为此，本文提出了一种基于最大余量准则和稀疏性的无监督领域自适应新领域不变特征学习方法，称为判别式和信息性联合分布自适应（DIJDA）。特别，DIJDA在适应过程中采用最大余量准则，以使转换后的样本接近相同类别的样本，但与不同类别的样本分开。结果，从源标签引用的区分性知识可以有效地转移到目标域。此外，DIJDA在转换矩阵上施加行稀疏约束，这将与非本质特征属性相对应的矩阵行强制全部为零。因此，可以提取最有用的特征属性。与几种最新方法相比，DIJDA大大改善了五个广泛使用的基准数据集的分类结果，从而证明了该方法的有效性。