首页|Dictionary-based transfer learning with Universum data
Dictionary-based transfer learning with Universum data
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NSTL
Elsevier
? 2022 Elsevier Inc.Recently, transfer learning is a popular method in machine learning, which transfers the knowledge learned from source task into target task. In practice, we can obtain the third-class examples except for the positive samples or negative samples, which are called Universum data, and Universum data can improve the performance of the classifier. In this paper, we propose a dictionarybased transfer learning with Universum data method, named U-DTL. In the proposed method, we first introduce the Universum data into the proposed model by the ?-insensitive loss. We then embed two dictionaries for the source and target domains into a new model, and put forward the similarity constraint for dictionaries between both domains to determine the relationship among samples of source and target domains. Further, we use the gradient-based optimization and SVD algorithm to alternately optimize and update the dictionaries, and utilize Lagrangian function to iteratively optimize the proposed U-DTL model to obtain the classifier. Finally, the statistic result of Wilcoxon-test has shown that the proposed U-DTL method has the better performance than previous methods. And we have conducted extensive experiments on the benchmark datasets to evaluate the performance of the proposed U-DTL method and baselines. The results show that the proposed U-DTL method makes the better performance than previous methods.
Dictionary learningTransfer learningUniverisum data
Che Z.、Liu B.、Lin L.、Xiao Y.
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School of Automation Guangdong University of Technology
School of Computer Science Guangdong University of Technology
NSTL
Elsevier
