An improvement on learning with local and global consistency
An improvement on learning with local and global consistency
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DOI：
10.1109/icpr.2008.4761295
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Jie Gui;De-shuang Huang;Zhuhong You
中科院分区：
文献类型：
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作者：
Jie Gui;De-shuang Huang;Zhuhong You
提出了一种改进的局部和全局一致性半监督学习算法。该方法增加了标签信息，在计算时采用测地距离而不是欧氏距离作为两个数据点之间差值的度量。此外，我们还添加了类先验知识。研究发现，在高标签率和低标签率下，类别先验知识的作用不同。实验结果表明，改进后的算法比原算法具有更好的分类性能。
A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. The new method adds the label information, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting calculation. In addition we add class prior knowledge. It was found that the effect of class prior knowledge was different between under high label rate and low label rate. The experimental results show that the changes attain the satisfying classification performance better than the original algorithms.