Multi-transfer: Transfer learning with multiple views and multiple sources

Transfer learning, which aims to help the learning task in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications, e.g., text mining, sentiment analysis, etc. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and usually carried by multiple sources. For example, to help classify videos on Youtube, which include three views/perspectives: image, voice and subtitles, one may borrow data from Flickr, Last.FM and Google News. Although any single instance in these domains can only cover a part of the views available on Youtube, actually the piece of information carried by them may compensate with each other. In this paper, we define this transfer learning problem as Transfer Learning with Multiple Views and Multiple Sources. As different sources may have different probability distributions and different views may be compensate or inconsistent with each other, merging all data in a simplistic manner will not give optimal result. Thus, we propose a novel algorithm to leverage knowledge from different views and sources collaboratively, by letting different views from different sources complement each other through a co-training style framework, while revise the distribution differences in different domains. We conduct empirical studies on several real-world datasets to show that the proposed approach can improve the classification accuracy by up to 8% against different state-of-the-art baselines.