Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers

In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurrence data which can be easily collected from web pages and social networks. We also construct an online classifier based on data from a target domain, and combine the offline and online classifiers by using the Hedge weighting strategy to update their weights for ensemble prediction. The theoretical analysis of error bound of the proposed algorithm is provided. Experiments on a real-world data set demonstrate the effectiveness of the proposed algorithm.