Label Information Guided Graph Construction for Semi-Supervised Learning

Label Information Guided Graph Construction for Semi-Supervised Learning
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半监督学习的标签信息引导图构建

DOI:
10.1109/tip.2017.2703120
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发表时间:
2017-05
影响因子:
10.6
通讯作者:
Ma Yi
Ma Yi
中科院分区:
计算机科学1区
文献类型:
--
作者:
Zhuang Liansheng;Zhou Zihan;Gao Shenghua;Yin Jingwen;Lin Zhouchen;Ma Yi

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在文献中,大多数现有的基于图的半监督学习方法在标签传播阶段仅使用观测样本的标签信息,而在学习图时忽略了此类有价值的信息。在本文中,我们认为……
In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.
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