Learn structured analysis discriminative dictionary for multi-label classification

Multi-label learning is a machine learning classification problem, in which an example belongs to more than one classes at the same time. Recently, multi-label learning has aroused a great deal of attention, and has achieved great success in the fields of text and image classification. In this paper, we propose a new method for multi-label learning, which is named as analysis discriminative dictionary learning for multi-label classification (ADML). We first incorporate analytical discrimination dictionary learning and sparse representation into multi-label classifier to obtain a unified model. The incoherence promoting term and reconstruction error for each label are minimized to obtain the dictionary. We then incorporate an analysis inconsistency promotion term into the model, which minimizes the reconstruction error of the dictionary with the corresponding label of the data. Further, we calculate a linear classifier by taking the label relationships into account. It is worth noting that we implicitly consider the label relationships in the analysis dictionary and linear classifier. Finally, we conduct experiments on 15 datasets to test the performance of the proposed ADML method and baselines. The results show that the proposed ADML method can deliver higher performance than previous multi-label methods.

Automated summary

The paper introduces a new method called ADML for multi-label learning, which combines analytical discrimination dictionary learning and sparse representation to achieve success in multi-label classification.