Domain adaptation aims to deal with learning problems in which the labeled training data and unlabeled testing data are differently distributed. Maximum mean discrepancy (MMD), as a distribution distance measure, is minimized in various domain adaptation algorithms for eliminating domain divergence. We analyze empirical MMD from the point of view of graph embedding. It is discovered from the MMD intrinsic graph that, when the empirical MMD is minimized, the compactness within each domain and each class is simultaneously reduced. Therefore, points from different classes may mutually overlap, leading to unsatisfactory classification results. To deal with this issue, we present a graph embedding framework with intrinsic and penalty graphs for MMD-based domain adaptation algorithms. In the framework, we revise the intrinsic graph of MMD-based algorithms such that the within-class scatter is minimized, and thus, the new features are discriminative. Two strategies are proposed. Based on the strategies, we instantiate the framework by exploiting four models. Each model has a penalty graph characterizing certain similarity property that should be avoided. Comprehensive experiments on visual cross-domain benchmark datasets demonstrate that the proposed models can greatly enhance the classification performance compared with the state-of-the-art methods.

中文翻译：

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基于最大均值差异的域自适应算法的图嵌入框架。

领域适应旨在解决学习问题，其中标记的训练数据和未标记的测试数据以不同的方式分布。为了消除域差异，在各种域自适应算法中将作为分布距离度量的最大平均差异（MMD）最小化。我们从图形嵌入的角度分析经验MMD。从MMD内在图发现，当经验MMD最小化时，每个域和每个类别内的紧密度都会同时降低。因此，不同类别的点可能会相互重叠，导致分类结果不理想。为了解决这个问题，我们为基于MMD的域自适应算法提供了一个具有内在图和惩罚图的图嵌入框架。在框架中，我们修改了基于MMD的算法的内在图，以使类内散布最小化，从而使新功能具有区分性。提出了两种策略。基于这些策略，我们通过开发四个模型来实例化该框架。每个模型都有一个惩罚图，描述某些应避免的相似性。在视觉跨域基准数据集上的综合实验表明，与最新方法相比，所提出的模型可以大大提高分类性能。每个模型都有一个惩罚图，描述某些应避免的相似性。在视觉跨域基准数据集上的综合实验表明，与最新方法相比，所提出的模型可以大大提高分类性能。每个模型都有一个惩罚图，描述某些应避免的相似性。在视觉跨域基准数据集上的综合实验表明，与最新方法相比，所提出的模型可以大大提高分类性能。