Multi-label learning with label-specific features by resolving label correlations

Highlights：

• We propose to learn label-specific features using sparsity regularized optimization in multi-label setting, which cover the information of label correlations.

• We model this multi-label learning problem by an optimization framework in which the weights of features and label correlations-based features are defined as two sets of unknown variables, and introduce a iterative optimization method to update these unknown variables.

• Label correlations are represented by additional features generated in the optimization process, and a KNN-like method is designed to obtain label correlations-based features of test data.

• Extensive experiments demonstrate the advantages of our proposed algorithm. In addition, two real-world data sets on TCM are collected, and our proposed algorithm is further validated on these two data sets in terms of the identification of health-state in TCM.