Manifold-based Similarity Adaptation for Label Propagation
Manifold-based Similarity Adaptation for Label Propagation
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发表时间：
2013-12
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通讯作者：
Masayuki Karasuyama;Hiroshi Mamitsuka
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作者：
Masayuki Karasuyama;Hiroshi Mamitsuka
标签传播是半监督学习的最先进方法之一，它通过在图中传播标签信息来估计标签。标签传播假设图中连接的数据点（节点）应该具有相似的标签。因此，标签估计在很大程度上依赖于图中的边权重，该边权重表示每个节点对的相似性。我们提出了一种方法，一个图形捕捉输入功能的流形结构，使用相似性函数参数化的边权重。在这种方法中，边缘权重同时表示相似性和局部重建权重，两者对于标签传播都是合理的。为了进一步的理由，我们提供了分析的考虑，包括解释为在特征空间中的传播模型的交叉验证，以及基于低维流形模型的误差分析。实验结果表明，我们的方法在合成和真实的数据集的有效性。
Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Label propagation assumes that data points (nodes) connected in a graph should have similar labels. Consequently, the label estimation heavily depends on edge weights in a graph which represent similarity of each node pair. We propose a method for a graph to capture the manifold structure of input features using edge weights parameterized by a similarity function. In this approach, edge weights represent both similarity and local reconstruction weight simultaneously, both being reasonable for label propagation. For further justification, we provide analytical considerations including an interpretation as a cross-validation of a propagation model in the feature space, and an error analysis based on a low dimensional manifold model. Experimental results demonstrated the effectiveness of our approach both in synthetic and real datasets.