A general framework for scalable transductive transfer learning

• Published in: Knowledge and information systems Vol. 38; no. 1; pp. 61 - 83
• Main Authors: Bahadori, Mohammad Taha, Liu, Yan, Zhang, Dan
• Format: Journal Article
• Language: English
• Published: London Springer London 2014; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Applied sciences; Artificial intelligence; Classification; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data mining; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Datasets; Exact sciences and technology; Filtering; Filtration; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Intelligent systems; IT in Business; Learning; Learning and adaptive systems; Memory and file management (including protection and security); Memory organisation. Data processing; Regular Paper; Software; State of the art; Stochastic models; Studies; Support vector machines; Stochastic gradient descent; Large-margin approach; Transductive transfer learning; Rademacher complexity; Mobile phone; On line; Electronic mail; Data mining; Inductive learning; Spam; Gradient descent; Vector support machine; Learning algorithm; Computer security; Complexity class; Filtering; Residence time; Transductive learning; Social network; Knowledge management; Case based reasoning; Computational complexity; Knowledge transfer; Web site; Artificial intelligence
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-013-0647-5

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.