Discriminative Joint Matching for Unsupervised Domain Adaptation
ZHANG Yong1,2, XIA Tianqi1, HUANG Dan1
1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116081 2. School of Information Engineering, Huzhou University, Hu-zhou 313000
Abstract The transfer effect of domain adaption is poor due to the large differences between domains. It can be improved by reducing the domain difference. However, the discriminability of later classification is ignored. A discriminative joint matching algorithm is proposed to handle this problem. Differentiation treatments are conducted according to different categories between domains. Feature matching and instance reweighting are combined to improve the migration effect. The joint probability distribution is employed to measure the difference of data distribution between domains. The transferability is enhanced by reducing the distance between the same domains. The discriminability is improved by expanding the distance between different domains. Feature matching and instance weighting are combined in the process of feature dimensionality reduction to jointly construct a feature transformation matrix. The experimental results show that the classification result of the proposed algorithm on 18 tasks is better.
Fund:National Natural Science Foundation of China(No.61772252), Natural Science Foundation of Liaoning Province(No.2019-MS-216), Scientific Research Foundation of the Education Department of Liaoning Province(No.LJKZ0965)
Corresponding Authors:
ZHANG Yong, Ph.D., professor. His research interests include data mining and intelligent computing.
About author:: XIA Tianqi, master student. Her research interests include machine learning and intelligent computing. HUANG Dan, Ph.D., lecturer. Her research interests include data mining and pa-ttern recognition.
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