利用领域特征和独立性最大化学习领域不变子空间
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
摘要：
当训练数据和测试数据的分布不同时，领域自适应算法是有用的。本文研究了传感器和测量领域中的仪器变化和时变漂移问题，这些变化可以看作是特征空间中离散和连续的分布变化。我们提出了最大独立领域自适应（MIDA）和半监督MIDA（SMIDA）来解决这个问题。首先定义域特征来描述样本的背景信息，例如设备标签和采集时间。然后，MIDA学习一个子空间，该子空间与领域特征具有最大的独立性，以减少域间分布的差异。为了提高自适应性，还设计了一种特征增强策略，根据样本的背景对样本进行投影。该算法灵活、快速。通过对合成数据集和四个真实世界的传感器，测量和计算机视觉的实验验证了它们的有效性。该算法可以统一处理不同类型的分布变化，从而大大提高传感器系统的实用性，扩展现有领域自适应算法的应用范围。
英文摘要：
Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA (SMIDA) to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns a subspace which has maximum independence with the domain features, so as to reduce the inter-domain discrepancy in distributions. A feature augmentation strategy is also designed to project samples according to their backgrounds so as to improve the adaptation. The proposed algorithms are flexible and fast. Their effectiveness is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision. They can greatly enhance the practicability of sensor systems, as well as extend the application scope of existing domain adaptation algorithms by uniformly handling different kinds of distributional change.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
