论文标题
过滤的歧管对准
Filtered Manifold Alignment
论文作者
论文摘要
域的适应性是转移学习以利用一个域中的数据来支持另一个域中学习的一项重要任务。在本文中，我们提出了一种新的半监督的歧管对准技术，基于两步方法的方法，该方法将源和目标域和目标域的投影和过滤到低维空间，然后连接两个空间。我们提出的方法，过滤的歧管比对（FMA）降低了以前的歧管对准技术的计算复杂性，足以使其具有完全不同的特征集与完全不同的特征集对齐域，并演示了在多个基准标记域适应任务上组成现实世界图像数据集的多个基准标记域适应任务。
Domain adaptation is an essential task in transfer learning to leverage data in one domain to bolster learning in another domain. In this paper, we present a new semi-supervised manifold alignment technique based on a two-step approach of projecting and filtering the source and target domains to low dimensional spaces followed by joining the two spaces. Our proposed approach, filtered manifold alignment (FMA), reduces the computational complexity of previous manifold alignment techniques, is flexible enough to align domains with completely disparate sets of feature and demonstrates state-of-the-art classification accuracy on multiple benchmark domain adaptation tasks composed of classifying real world image datasets.
