Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 4, Pages 2166-2177Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2957033
Keywords
Kernel; Transforms; Training; Computational modeling; Support vector machines; Covariance matrices; Data mining; Domain adaptation (DA); feature learning; feature translator; heterogeneous data; optimal experimental design
Categories
Funding
- National Natural Science Foundation of China [61976229, 61572536, 61906046, 11631015, U1611265]
- Science and Technology Program of Guangzhou [201804010248]
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This article focuses on domain adaptive feature representation for heterogeneous data by introducing projection matrices and a joint kernel regression model. It aims to efficiently transfer discriminant information from source to target domain, enhancing classification performance for the target data.
Domain adaptation (DA) and transfer learning with statistical property description is very important in image analysis and data classification. This article studies the domain adaptive feature representation problem for the heterogeneous data, of which both the feature dimensions and the sample distributions across domains are so different that their features cannot be matched directly. To transfer the discriminant information efficiently from the source domain to the target domain, and then enhance the classification performance for the target data, we first introduce two projection matrices specified for different domains to transform the heterogeneous features into a shared space. We then propose a joint kernel regression model to learn the regression variable, which is called feature translator in this article. The novelty focuses on the exploration of optimal experimental design (OED) to deal with the heterogeneous and nonlinear DA by seeking the covariance structured feature translators (CSFTs). An approximate and efficient method is proposed to compute the optimal data projections. Comprehensive experiments are conducted to validate the effectiveness and efficacy of the proposed model. The results show the state-of-the-art performance of our method in heterogeneous DA.
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