  The major challenge of learning from multi-label data has arisen from the
overwhelming size of label space which makes this problem NP-hard. This problem
can be alleviated by gradually involving easy to hard tags into the learning
process. Besides, the utilization of a diversity maintenance approach avoids
overfitting on a subset of easy labels. In this paper, we propose a self-paced
multi-label learning with diversity (SPMLD) which aims to cover diverse labels
with respect to its learning pace. In addition, the proposed framework is
applied to an efficient correlation-based multi-label method. The non-convex
objective function is optimized by an extension of the block coordinate descent
algorithm. Empirical evaluations on real-world datasets with different
dimensions of features and labels imply the effectiveness of the proposed
predictive model.
