Multi-bridge transfer learning

• MBTL constructs multiple latent spaces to exploit more common latent factors.
• MBTL reduces the discrepancies of the distributions in different latent spaces.
• To solve MBTL, we present an iterative algorithm with convergence guarantee.
• MBTL outperforms state-of-the-art learning methods on several datasets.

Transfer learning, which aims to exploit the knowledge in the source domains to promote the learning tasks in the target domains, has attracted extensive research interests recently. The general idea of the previous approaches is to model the shared structure in one latent space as the bridge across domains by reducing the distribution divergences. However, there exist some latent factors in the other latent spaces, which can also be utilized to draw the corresponding distributions closer for establishing the bridges. In this paper, we propose a novel transfer learning method, referred to as Multi-Bridge Transfer Learning (MBTL), to learn the distributions in the different latent spaces together. Therefore, more latent factors shared can be utilized to transfer knowledge. Additionally, an iterative algorithm with convergence guarantee based on non-negative matrix tri-factorization techniques is proposed to solve the optimization problem. Comprehensive experiments demonstrate that MBTL can significantly outperform state-of-the-art learning methods on the topic and sentiment classification tasks.