Paper Info
Title
Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning
Abstract
Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only \textit{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 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 state-of-the-art ones.
Year
DOI
Venue
2011
10.1109/ICDM.2011.92
Data Mining
Keywords
Field
DocType
computational complexity,information networks,learning (artificial intelligence),LATTL,Rademacher complexity,large-margin transductive transfer learning algorithm,microblogging mining,source domain,spam filtering,target domain
Transduction (machine learning),Data mining,Semi-supervised learning,Multi-task learning,Inductive transfer,Computer science,Rademacher complexity,Transfer of learning,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning
Conference
ISSN
ISBN
Citations 
1550-4786
978-1-4577-2075-8
15
PageRank 
References 
Authors
0.70
20
3
Name
Order
Citations
PageRank
Mohammad Taha Bahadori138319.60
Yan Liu22551189.16
Dan Zhang346122.17