关键词: 多标记数据, 迁移学习, 子空间学习, 联合分布

Abstract:

To improve the poor generalization of the existing multi-labels transfer learning, a Multi-label Transfer Learning via Joint Distribution Alignment（J-MLTL） is proposed. J-MLTL decomposes the original features to generate a feature subspace, and calculates the weight coefficients of the conditional distribution in the subspace. Finally, J-MLTL narrows the differences between domains by adjusting the marginal and conditional distribution of the data. In order to prevent the loss of internal information in the labels, J-MLTL uses hypergraph to connects data with multiple identical labels. Hypergraph keeps geometric popular structures in the domain from being affected by knowledge structures outside the domain, and further minimizes distribution differences between domains to ensure good results. The experimental results show that compared with the existing multi-label transfer learning algorithms, the classification accuracy is significantly improved.

Key words: multi-label data, transfer learning, subspace learning, joint distribution