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• W2398879410 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2012 SIAM International Conference on Data Mining (SDM)Dual Transfer LearningMingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, and Wei WangMingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, and Wei Wangpp.540 - 551Chapter DOI:https://doi.org/10.1137/1.9781611972825.47PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the target domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applications. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods usually model the common latent structure by using either the marginal distribution or the conditional distribution. However, without exploring the duality between these two distributions, these single bridge methods may not achieve optimal capability of knowledge transfer. In this paper, we propose a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the duality between them in a principled way. The key idea behind DTL is that learning one distribution can help to learn the other. This duality property leads to mutual reinforcement when adapting both distributions across domains to transfer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix trifactorizations (NMTF). The two distributions are learned from the decomposed latent factors that exhibit the duality property. An efficient alternating minimization algorithm is developed to solve the optimization problem with convergence guarantee. Extensive experimental results demonstrate that DTL is more effective than alternative transfer learning methods. Previous chapter Next chapter RelatedDetails Published:2012ISBN:978-1-61197-232-0eISBN:978-1-61197-282-5 https://doi.org/10.1137/1.9781611972825Book Series Name:ProceedingsBook Code:PRDT12Book Pages:1-1150Key words:Keywords Transfer Learning, Cross-Domain Classification, Dual Transfer Learning, Nonnegative Matrix Tri-Factorization" @default.
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• W2398879410 date "2012-04-26" @default.
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• W2398879410 title "Dual Transfer Learning" @default.
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• W2398879410 doi "https://doi.org/10.1137/1.9781611972825.47" @default.
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