NDLI: Transfer metric learning for unsupervised domain adaptation

Content Provider	IET Digital Library
Author	Huang, Junchu Zhou, Zhiheng
Abstract	Domain adaptation is still a challenging task due to the fact that the distribution discrepancy between source domain and target domain weakens the transfer ability. Intuitively, it is crucial to discover a more discriminative feature representation across domains. However, previous methods do not take the target discriminative information into account since (most) target data are unlabelled. Here, the authors propose a transfer metric learning method which decreases intra-class distance and increases inter-class distance simultaneously even in the case of target data are unlabelled. The shared features are more discriminative, hence the model could be more robust for target data. Specially, the global optimal solution can be obtained by solving a generalised eigen-decomposition problem. Extensive experiments on image datasets demonstrate that compared to several state-of-the-art methods, authors’ method achieves significant improvement of 9.0% in average classification accuracy.
Starting Page	804
Ending Page	810
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 5, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5871
Journal	IET Image Processing
Publisher Date	2019-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Computer Vision And Image Processing Technique Discriminative Feature Representation Distribution Discrepancy Feature Extraction Image Classification Image Recognition Image Representation Increases Inter-class Distance Intra-class Distance Knowledge Engineering Technique Learning in AI Optical, Image And Video Signal Processing Pattern Classification Previous Method Shared Feature Source Domain State-of-the-art Method Target Data Target Discriminative Information Target Domain Transfer Ability Transfer Metric Learning Method Unsupervised Domain Adaptation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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