A Self-Paced Regularization Framework for Multi-Label Learning
This work addresses multi-label classification, a common challenge in machine learning, by introducing an incremental improvement to existing self-paced learning methods.
The paper tackles the problem of multi-label learning by proposing a self-paced regularization framework that gradually includes tasks and instances from easy to hard, achieving state-of-the-art performance on three benchmark datasets.
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.