A general framework for scalable transductive transfer learning
Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only unlabeled examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining, and so on. In t...
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Published in | Knowledge and information systems Vol. 38; no. 1; pp. 61 - 83 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
2014
Springer Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only
unlabeled
examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining, and so on. In this paper, we propose a general framework to solve the problem by mapping the input features in both the source domain and the target domain into a shared latent space and simultaneously minimizing the feature reconstruction loss and prediction loss. We develop one specific example of the framework, namely latent large-margin transductive transfer learning algorithm, and analyze its theoretic bound of classification loss via Rademacher complexity. We also provide a unified view of several popular transfer learning algorithms under our framework. Experiment results on one synthetic dataset and three application datasets demonstrate the advantages of the proposed algorithm over the other state-of-the-art ones. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0219-1377 0219-3116 |
DOI: | 10.1007/s10115-013-0647-5 |