Semisupervised learning using feature selection based on maximum density subgraphs
Semisupervised learning using feature selection based on maximum density subgraphs
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DOI：
10.1002/scj.20757
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Yoshiyuki Nakatani;Kuangyi Zhu;Kuniaki Uehara
中科院分区：
文献类型：
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作者：
Yoshiyuki Nakatani;Kuangyi Zhu;Kuniaki Uehara
提出了一种新的基于图的半监督学习算法，利用邻域图上的多路切来实现最优分类。我们还提出了一种基于图的特征选择算法，该算法利用了来自标记和未标记示例的图的全局结构。通过我们的实验，我们的两种方法在提高学习精度方面都有很好的表现。
We present a new graph based semi-supervised learning algorithm, using multiway cut on a neighborhood graph to achieve an optimum classification. We also present a graph based feature selection algorithm utilizing the global structure of the graph derived from both labeled and unlabeled examples. With respect to the experiments we conducted, both of our approaches are proved to have a promising performance on the improvement of the learning accuracy.