Soft large margin clustering for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) methods usually perform feature matching between domains by considering the domain shift. However, the cluster structure of data, which is one focus in traditional unsupervised learning, is not considered in those methods. In this paper, we attempt to explore suc...

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Bibliographic Details
Published in:Knowledge-based systems 2020-03, Vol.192, p.105344, Article 105344
Main Authors: Wang, Yunyun, Nie, Lingli, Li, Yun, Chen, Songcan
Format: Article
Language:English
Subjects:
Adaptation
Cluster structure
Clustering
Domain shift
Domains
Optimization
Soft large margin clustering
Unsupervised domain adaptation
Unsupervised learning
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Summary:Unsupervised domain adaptation (UDA) methods usually perform feature matching between domains by considering the domain shift. However, the cluster structure of data, which is one focus in traditional unsupervised learning, is not considered in those methods. In this paper, we attempt to explore such cluster structure in UDA. Specifically, a general transfer learning framework called Clustering for Domain Adaptation (DAC) has been proposed. DAC explores the cluster structure of target data with the help of source data. It seeks a domain-invariant classifier by simultaneously reducing the distribution shifts between domains and exploring the cluster structure for target instances. The optimization of DAC adopts the ADMM strategy, in which each iteration generates a closed-form solution. Empirical results demonstrate the effectiveness of DAC over several real datasets.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.105344