Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
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DOI：
10.1016/j.patcog.2015.10.006
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Bo Wang-;Z. Tu;John K. Tsotsos
中科院分区：
文献类型：
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作者：
Bo Wang-;Z. Tu;John K. Tsotsos
在基于图的半监督学习方法中，分类率高度依赖于可用标记数据的大小，以及相似度度量的准确性。本文提出了一种半监督多类/多标签分类方案——动态标签传播（dynamic label propagation， DLP），该方案通过在动态过程中传播来进行换能化学习。现有的半监督分类方法由于缺乏对标签相关性的考虑，往往难以处理多类/多标签问题；我们的算法强调与标签信息的动态度量融合。在多类和多标签任务的基准数据集上观察到比最先进的方法有显著改进。
In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic label propagation (DLP), which performs transductive learning through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation; our algorithm instead emphasizes dynamic metric fusion with label information. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multiclass and multi-label tasks.