Abstract: Multi-label learning deals with the problem where each instance is associated with a set of class labels. In multilabel learning, different labels may have their own inherent characteristics for distinguishing each other, and the correlation information has shown promising strength in improving multi-label learning. In this study, we propose a novel multilabel learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process. Firstly, we learn a sparse weight parameter vector for each label based on the linear regression model, and the label-specific features can be extracted according to the corresponding weight parameters. Secondly, we constrain label correlations directly on the output of labels, not on the corresponding parameter vectors which conflicts with the label-specific feature learning. Specifically, for any two related labels, their corresponding models should have similar outputs rather than similar parameter vectors. Thirdly, we also exploit the sample correlations through sparse reconstruction. The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods. The proposed method ranks in the 1st place at 66.7% case and achieves optimal average rank in terms of all evaluation measures.