Multi-source transfer learning based on label shared subspace

• A novel multi-labeling of samples for multi-source samples is proposed.
• The shared subspace among labels for multi-sources is proposed.
• A novel multi-source transfer learning is presented based on label shared subspace.

Multi-source transfer learning focuses on studying the scarcity of samples with labels in target domain, while neglecting the analysis about transferability relationship among multiple source domains. Thus, we propose a method that transforms samples in target domain into multi-label samples, with which it is able to analyze the correlations among predicted labels from different sources. We design a method that can extract the shared subspace among labels in multi-sources, and propose a novel multi-source transfer learning method based on multi-label shared subspace. This approach is required when knowledge about multiple sources are available but it is unknown which source is of more transferability. Experiments show that our proposed algorithm can improve the performance of transfer learning method and alleviate time complexity.