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Title: A general domain specific feature transfer framework for hybrid domain adaptation
Authors: Wei, Pengfei
Ke, Yiping
Goh, Chi Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Wei, P., Ke, Y., & Goh, C. K. (2018). A general domain specific feature transfer framework for hybrid domain adaptation. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1440-1451. doi:10.1109/TKDE.2018.2864732
Journal: IEEE Transactions on Knowledge and Data Engineering 
Abstract: Heterogeneous domain adaptation needs supplementary information to link up different domains. However, such supplementary information may not always be available in real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation, in which different domains share some common features, but also have their own domain specific features. We leverage upon common features instead of supplementary information to achieve effective adaptation. We propose a general domain specific feature transfer framework, which can link up different domains using common features and simultaneously reduce domain divergences. Specifically, we learn the translations between common features and domain specific features. Then, we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. We instantiate the general framework to a linear case and a nonlinear case. Extensive experiments verify the effectiveness of the two cases.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2864732
Schools: School of Computer Science and Engineering 
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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