There is a growing importance of feature extraction in transferring valuable knowledge from a source domain to a different but related target domain. However, when the target data are contaminated by unpredictable and complex noises, the ability of most existing feature extraction methods would be limited. In this paper, we deeply investigate the robust property of Kernel Mean P-Power Error Loss (KMPE-Loss), and thus propose a novel Robust Transfer Feature Learning (RTFL) method to enhance the robustness of domain adaptation. The key idea of RTFL is to learn a shared transformation by: 1) detecting and neglecting the contaminated target points without any specific assumption on noises; 2) reconstructing the remaining clean target points using the corresponding source-domain neighborhood; 3) incorporating a relative entropy based regularization to reap theoretic advantages. Consequently, the distribution difference between two domains is accurately reduced for knowledge transfer. We propose an alternative procedure to optimize RTFL with explicitly guaranteed convergence. As an extension, the transformation based matrix in RTFL is restricted to a small dimension basis, admitting the highly reduced computation complexity. Extensive experiments in various domain adaptation tasks demonstrate the superiority of our methods.

在将有价值的知识从源域转移到另一个但相关的目标域中时，特征提取的重要性越来越高。然而，当目标数据被不可预测的复杂噪声污染时，大多数现有特征提取方法的能力将受到限制。在本文中，我们深入研究了内核平均P功率误差损失（KMPE-Loss）的鲁棒性，从而提出了一种新颖的鲁棒传递特征学习（RTFL）方法来增强域自适应的鲁棒性。RTFL的主要思想是通过以下方法学习共享的变换：1）在不对噪声进行任何特定假设的情况下，检测并忽略受污染的目标点；2）使用相应的源域邻域重建剩余的干净目标点；3）结合基于相对熵的正则化以获得理论优势。因此，可以准确地减小两个域之间的分布差异以进行知识转移。我们提出了另一种程序，以明确保证收敛的方式优化RTFL。作为扩展，RTFL中基于变换的矩阵被限制在较小的维度上，从而大大降低了计算复杂性。在各种领域适应任务中进行的大量实验证明了我们方法的优越性。RTFL中基于变换的矩阵被限制在小尺寸的基础上，从而大大降低了计算复杂度。在各种领域适应任务中进行的大量实验证明了我们方法的优越性。RTFL中基于变换的矩阵被限制在小尺寸的基础上，从而大大降低了计算复杂度。在各种领域适应任务中进行的大量实验证明了我们方法的优越性。