Feuz Kyle D, Cook Diane J
School of Electrical Engineering and Computer Science, Washington State University.
ACM Trans Intell Syst Technol. 2015 Apr;6(1). doi: 10.1145/2629528.
Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature- Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of meta-features. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces.
迁移学习旨在通过利用从源任务中学到的先前知识来提高目标任务的性能。在本文中，我们介绍了一种新颖的异构迁移学习技术——特征空间重映射（FSR），它能在具有不同特征空间的域之间传递知识。这一过程无需典型的特征-特征、特征实例或实例-实例共现数据即可完成。相反，我们通过构建元特征来关联不同特征空间中的特征。我们展示了这些技术如何利用多个源数据集来构建一个集成学习器，从而进一步提高性能。我们将FSR应用于活动识别问题和文档分类问题。该集成技术能够超越所有其他基线，甚至比使用目标域中大量标记数据训练的分类器表现更好。这些问题尤其困难，因为除了具有不同的特征空间外，边际概率分布和类别标签也不同。这项工作通过考虑在截然不同的空间之间进行大规模迁移，扩展了迁移学习的技术水平。
