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Research Article | Open Access

Structure preserved ordinal unsupervised domain adaptation

Qing Tian1,2,3(), Canyu Sun1
School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Wuxi Institute of Technology, Nanjing University of Information Science and Technology, Wuxi 214000, China
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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Abstract

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from labeled source domain to unlabeled target domain. The main challenge of UDA stems from the domain shift between the source and target domains. Currently, in the discrete classification problems, most existing UDA methods usually adopt the distribution alignment strategy while enforcing unstable instances to pass through the low-density areas. However, the scenario of ordinal regression (OR) is rarely researched in UDA, and the traditional UDA methods cannot preferably handle OR since they do not preserve the order relationships in data labels, like in human age estimation. To address this issue, we proposed a structure-oriented adaptation strategy, namely, structure preserved ordinal unsupervised domain adaptation (SPODA). More specifically, on one hand, the global structure information was modeled and embedded into an auto-encoder framework via a low-rank transferred structure matrix. On the other hand, the local structure information was preserved through a weighted pair-wise strategy in the latent space. Guided by both the local and global structure information, a well-performance latent space was generated, whose geometric structure was adopted to further obtain a more discriminant ordinal regressor. To further enhance its generalization, a counterpart of SPODA with deep architecture was developed. Finally, extensive experiments indicated that in addressing the OR problem, SPODA was more effective and advanced than existing related domain adaptation methods.

Keywords

unsupervised domain adaptationordinal domain adaptationstructure-oriented adaptationordinal regression

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Electronic Research Archive
Volume 32 Issue 11,
November 2024
Pages 6338-6363
DOI: 10.3934/era.2024295
Cite this article:
Tian Q, Sun C. Structure preserved ordinal unsupervised domain adaptation. Electronic Research Archive, 2024, 32(11): 6338-6363. https://doi.org/10.3934/era.2024295
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