A Self-Paced Regularization Framework for Multi-Label Learning
A Self-Paced Regularization Framework for Multi-Label Learning
复制标题
用于多标签学习的自定进度正则化框架
DOI：
10.1109/tnnls.2017.2697767
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发表时间：
2018
影响因子：
10.4
通讯作者：
Hongyuan Zha
中科院分区：
文献类型：
--
作者：
Changsheng Li;Fan Wei;Junchi Yan;Xiaoyu Zhang;Qingshan Liu;Hongyuan Zha
在本文中，我们提出了一种新颖的多标签学习框架，称为多标签自定进度学习，试图将 SPL 方案纳入多标签学习体系中。具体来说，我们首先通过引入自定进度函数作为正则化器，提出了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定进度方案，因此我们提供了一种通用的方法来找到所需的自定进度函数。据我们所知，这是第一个通过共同考虑训练实例和标签的复杂性来研究多标签学习的工作。与最先进的方法相比，四个公开数据集的实验结果表明了我们的方法的有效性。
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.