IEEE Trans Neural Netw Learn Syst. 2018 Jun;29(6):2660-2666. doi: 10.1109/TNNLS.2017.2697767. Epub 2017 May 16.
In this brief, we propose a novel multilabel learning framework, called multilabel self-paced learning, in an attempt to incorporate the SPL scheme into the regime of multilabel learning. Specifically, we first propose a new multilabel learning formulation by introducing a self-paced function as a regularizer, so as to simultaneously prioritize label learning tasks and instances in each iteration. Considering that different multilabel learning scenarios often need different self-paced schemes during learning, we thus provide a general way to find the desired self-paced functions. To the best of our knowledge, this is the first work to study multilabel learning by jointly taking into consideration the complexities of both training instances and labels. Experimental results on four publicly available data sets suggest the effectiveness of our approach, compared with the state-of-the-art methods.
在这篇简报中，我们提出了一种新颖的多标签学习框架，称为多标签自步学习，试图将 SPL 方案纳入多标签学习领域。具体来说，我们首先通过引入自步函数作为正则化项来提出一种新的多标签学习公式，以便在每次迭代中同时优先考虑标签学习任务和实例。考虑到不同的多标签学习场景在学习过程中通常需要不同的自步方案，因此我们提供了一种找到所需自步函数的通用方法。据我们所知，这是首次联合考虑训练实例和标签的复杂性来研究多标签学习的工作。在四个公开可用的数据集上的实验结果表明，与最先进的方法相比，我们的方法是有效的。
