  In this paper, we propose a novel multi-label learning framework, called
Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the
self-paced learning strategy into multi-label learning regime. In light of the
benefits of adopting the easy-to-hard strategy proposed by self-paced learning,
the devised MLSPL aims to learn multiple labels jointly by gradually including
label learning tasks and instances into model training from the easy to the
hard. We first introduce a self-paced function as a regularizer in the
multi-label learning formulation, so as to simultaneously rank priorities of
the label learning tasks and the instances in each learning iteration.
Considering that different multi-label learning scenarios often need different
self-paced schemes during optimization, we thus propose a general way to find
the desired self-paced functions. Experimental results on three benchmark
datasets suggest the state-of-the-art performance of our approach.
