Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning
Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning
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DOI：
10.1109/tkde.2019.2951561
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发表时间：
2019-11
影响因子：
8.9
通讯作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
中科院分区：
文献类型：
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作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
多标签学习处理训练示例，每个示例由单个实例表示，同时与多个类别标签相关联，任务是训练一个预测模型，该模型可以为未见过的实例分配一组适当的标签。现有的方法采用相同标签重要性的共同假设，即，所有相关联的标签都被认为与训练实例相关，而它们在表征其语义时的相对重要性不被区分。尽管如此，这种常见的假设并没有反映每个相关标签的重要程度通常不同的事实，尽管重要性信息不能直接从训练示例中获得。在这篇文章中，我们证明了利用隐含的相对标记重要性（RLI）信息来帮助诱导具有强泛化性能的多标记预测模型是有益的。具体来说，RLI度被形式化为标签空间上的多项式分布，可以通过全局标签传播过程或局部$k$k-最近邻重建来估计。相应地，多标签预测模型是通过将建模输出与估计的RLI度沿着以及多标签经验损失正则化来推导的。大量的实验清楚地验证了利用隐式RLI信息是实现有效多标签学习的有利策略。
Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels, and the task is to train a predictive model which can assign a set of proper labels for the unseen instance. Existing approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are regarded to be relevant to the training instance while their relative importance in characterizing its semantics are not differentiated. Nonetheless, this common assumption does not reflect the fact that the importance degree of each relevant label is generally different, though the importance information is not directly accessible from the training examples. In this article, we show that it is beneficial to leverage the implicit relative labeling-importance (RLI) information to help induce multi-label predictive model with strong generalization performance. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which can be estimated by either global label propagation procedure or local $k$k-nearest neighbor reconstruction. Correspondingly, the multi-label predictive model is induced by fitting modeling outputs with estimated RLI degrees along with multi-label empirical loss regularization. Extensive experiments clearly validate that leveraging implicit RLI information serves as a favorable strategy to achieve effective multi-label learning.