Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels
Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels
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DOI：
10.1109/icdm.2014.125
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Linli Xu;Zhen Wang;Zefan Shen;Yubo Wang;Enhong Chen
中科院分区：
文献类型：
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作者：
Linli Xu;Zhen Wang;Zefan Shen;Yubo Wang;Enhong Chen
多标签学习处理的问题是，每个训练样本同时与一组标签相关联，这组标签对应于多个概念或语义。直观地说，多个标签通常在共享同一输入空间的同时在某个语义空间中是相关的。因此，多标签学习过程可以通过有效地利用标签相关性来显着增强。大多数现有的方法共享的局限性，标签相关性通常被视为先验知识，这可能无法正确地描述标签之间的真实依赖关系，或者他们没有充分解决标签丢失的问题。在本文中，我们提出了一个集成框架，在学习标签之间的相关性的同时训练多标签模型。具体来说，采用低秩结构来捕获标签之间的复杂相关性。此外，我们还引入了一个补充标签矩阵，通过利用标签相关性来增强可能不完整的标签矩阵。交替算法，然后开发解决优化问题。大量的实验进行了大量的图像和文本数据集，以证明所提出的方法的有效性。
Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach.