Label missing and class imbalance problems are two hot research topics in machine learning, and they have been impeding the improvement of model performance, especially in the multi-label learning. Although some existing methods have proven to be effective, they are suitable for only one case. How to effectively address above two issues simultaneously is a challenging problem. In this paper, we propose a novel model named Imbalanced and Missing multi-Label data learning with Global and Local structure (IMLGL) to address the aforementioned challenge. There are following three advantages. At the empirical risk level, we introduce the label correlation matrix C into the loss function and devise a dynamic weighting method to address the aforementioned challenge. At the data level, we analyze the structural characteristics of the data, and introduce local low-rank and global high-rank term to enhance the generalization performance of the model. At the label level, a smoothing term is also introduced for learning the constraint classifier coefficient matrix W. Our method utilizes alternative optimization technique and alternating minimization method for solving. Extensive experiments on six datasets demonstrate the competitiveness of our approach.
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