Domain adaptation (DA) is transfer learning which aims to learn an effective
predictor on target data from source data despite data distribution mismatch
between source and target. We present in this paper a novel unsupervised DA
method for cross-domain visual recognition which simultaneously optimizes the
three terms of a theoretically established error bound. Specifically, the
proposed DA method iteratively searches a latent shared feature subspace where
not only the divergence of data distributions between the source domain and the
target domain is decreased as most state-of-the-art DA methods do, but also the
inter-class distances are increased to facilitate discriminative learning.
Moreover, the proposed DA method sparsely regresses class labels from the
features achieved in the shared subspace while minimizing the prediction errors
on the source data and ensuring label consistency between source and target.
Data outliers are also accounted for to further avoid negative knowledge
transfer. Comprehensive experiments and in-depth analysis verify the
effectiveness of the proposed DA method which consistently outperforms the
state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain
image classification tasks.

本文提出了一种新颖的无监督域自适应方法，通过同时优化理论上建立的误差界的三个项，迭代搜索潜在的共享特征子空间，来降低数据分布差异并增加类间距离，以有效学习目标数据的预测器。同时，还考虑了数据异常值的影响以避免负面知识迁移。实验和分析验证了该方法的有效性和优越性。