Multi-label learning deals with the problem where each instance has a set of class labels simultaneously. In multi-label learning

label correlations have shown promising strength in improving multi-label learning. Most of the existing multi-label learning algorithms exploited either global label correlations shared among all instances

or local label correlations varied across different clusters of instances. In this study

we propose a novel multi-label learning method by simultaneously taking into account both the global and local label correlations to capture more comprehensive label information during the learning process. To calculating global and local label correlations

we utilize cosine similarity to obtain positive and negative correlations between different labels

which helps us to further achieve more reliable multi-label learning. We implemented extensive experimental comparisons based on various data sets to validate the effectiveness of our algorithm. The experimental results show that the proposed algorithm significantly outperforms most of the state-of-the art approaches

demonstrating its prominent performance for multi-label learning.