Boosting for transfer learning from multiple data sources

Authors:
Pipei Huang;Gang Wang;Shiyin Qin
Affiliations:
School of Automation Science and Electrical Engineering, Beihang University, 100191 Beijing, China;Machine Learning Group, Microsoft Research Asia, Sigma Center, 49 Zhichun Road, 100190 Beijing, China;School of Automation Science and Electrical Engineering, Beihang University, 100191 Beijing, China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

Transfer learning aims at adapting a classifier trained on one domain with adequate labeled samples to a new domain where samples are from a different distribution and have no class labels. In this paper, we explore the transfer learning problems with multiple data sources and present a novel boosting algorithm, SharedBoost. This novel algorithm is capable of applying for very high dimensional data such as in text mining where the feature dimension is beyond several ten thousands. The experimental results illustrate that the SharedBoost algorithm significantly outperforms the traditional methods which transfer knowledge with supervised learning techniques. Besides, SharedBoost also provides much better classification accuracy and more stable performance than some other typical transfer learning methods such as the structural correspondence learning (SCL) and the structural learning in the multiple sources transfer learning problems.