DMCA
Learning with minimum supervision: A general framework for transductive transfer learning (2011)
| Venue: | in ‘Proceeding of the 11th IEEE International Conference on Data Mining (ICDM |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Bahadori11learningwith,
author = {Mohammad Taha Bahadori and Yan Liu and Dan Zhang},
title = {Learning with minimum supervision: A general framework for transductive transfer learning},
booktitle = {in ‘Proceeding of the 11th IEEE International Conference on Data Mining (ICDM},
year = {2011},
pages = {61--70}
}
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Abstract
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 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. I.
Keyphrases
general framework minimum supervision transductive transfer learning target domain source domain unified view abstract transductive transfer learning latent large-margin transductive transfer learning feature reconstruction loss prediction loss theoretic bound experiment result rademacher complexity application datasets several popular transfer state-of-the-art one input feature synthetic dataset unlabeled example spam filtering latent space transfer learning problem classification loss special type specific example
