Dual transfer learning

Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

63 Scopus citations


Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the tar- get domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applica- Tions. 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, with- out exploring the duality between these two distributions, these single bridge methods may not achieve optimal capa- bility 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 du- Ality 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 trans- fer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix tri- factorizations (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 con- vergence guarantee. Extensive experimental results demon- strate that DTL is more effective than alternative transfer learning methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages12
ISBN (Print)9781611972320
StatePublished - 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012


Other12th SIAM International Conference on Data Mining, SDM 2012
Country/TerritoryUnited States
CityAnaheim, CA

All Science Journal Classification (ASJC) codes

  • Computer Science Applications


Dive into the research topics of 'Dual transfer learning'. Together they form a unique fingerprint.

Cite this