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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach
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Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach

机译：来自多个源的域自适应：一种依赖于域的正则化方法

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摘要

In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.
机译：在本文中，我们针对多源域适应问题提出了一种称为域适应机（DAM）的新框架。在此框架下，我们通过利用一组基础分类器（通过使用源域或源域中的标记实例进行预学习）来学习用于目标域实例标签预测的强大决策函数（称为目标分类器）和目标域。使用基本分类器，我们基于平滑度假设提出了一个新的依赖于域的正则化器，该规则器强制目标分类器与目标域中未标记实例上的相关基本分类器共享相似的决策值。该新提出的正则化器可以很容易地并入许多内核方法中（例如，支持向量机（SVM），支持向量回归和最小二乘SVM（LS-SVM））。对于域适应，我们还开发了两种新的域适应方法，称为FastDAM和UniverDAM。在FastDAM中，我们将建议的与域相关的正则化方法引入LS-SVM，并使用稀疏性正则化方法仅从目标域中获取带有支持向量的稀疏目标分类器，从而使对任何测试实例的标签预测都非常快。在UniverDAM中，我们另外将源域中的实例用作Universum，以进一步增强目标分类器的泛化能力。我们在具有挑战性的TRECIVD 2005数据集（用于大规模视频概念检测任务）以及20个新闻组和电子邮件垃圾邮件数据集（用于文档检索）上评估了这两种方法。全面的实验表明，FastDAM和UniverDAM对于这两个应用程序的性能优于现有的多源域自适应方法。

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