Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning

Mohammad Taha Bahadori, Yan Liu, Dan Zhang
2011 2011 IEEE 11th International Conference on Data Mining  
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 target domain into a shared latent space and simultaneously minimizing the
more » ... y minimizing the feature reconstruction loss and prediction loss. We develop one specific example of the framework, namely latent large-margin transductive transfer learning (LATTL) 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 stateof-the-art ones.
doi:10.1109/icdm.2011.92 dblp:conf/icdm/BahadoriLZ11 fatcat:2chjvochtfdxdoly2kbadndexa