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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective
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Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective

机译：从多个来源进行跨域学习：共识正则化观点

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摘要

Classification across different domains studies how to adapt a learning model from one domain to another domain which shares similar data characteristics. While there are a number of existing works along this line, many of them are only focused on learning from a single source domain to a target domain. In particular, a remaining challenge is how to apply the knowledge learned from multiple source domains to a target domain. Indeed, data from multiple source domains can be semantically related, but have different data distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. To that end, in this paper, we propose a consensus regularization framework for learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in one source domain and the prediction consensus with the classifiers learned from other source domains. Moreover, we provide a theoretical analysis as well as an empirical study of the proposed consensus regularization framework. The experimental results on text categorization and image classification problems show the effectiveness of this consensus regularization learning method. Finally, to deal with the situation that the multiple source domains are geographically distributed, we also develop the distributed version of the proposed algorithm, which avoids the need to upload all the data to a centralized location and helps to mitigate privacy concerns.
机译：跨不同领域的分类研究了如何使学习模型从一个领域适应具有相似数据特征的另一个领域。尽管有很多现有的著作，但是其中许多著作只专注于从单个源域到目标域的学习。特别是，剩下的挑战是如何将从多个源域学到的知识应用于目标域。实际上，来自多个源域的数据可以在语义上相关，但是具有不同的数据分布。尚不清楚如何利用多个源域之间的分布差异来提高目标域中的学习性能。为此，在本文中，我们提出了一个共识正则化框架，用于从多个源域到目标域的学习。在此框架中，通过考虑一个源域中可用的本地数据以及与从其他源域中学习的分类器的预测共识来训练本地分类器。此外，我们提供了所提出的共识正则化框架的理论分析和实证研究。在文本分类和图像分类问题上的实验结果证明了这种共识正则化学习方法的有效性。最后，为了解决多个源域在地理上分散的情况，我们还开发了所提出算法的分布式版本，从而避免了将所有数据上传到集中位置的需求，并有助于缓解隐私问题。

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