A semi-supervised approach for dimensionality reduction with distributional similarity
A semi-supervised approach for dimensionality reduction with distributional similarity
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DOI：
10.1016/j.neucom.2012.09.023
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发表时间：
2013-03
期刊：
影响因子：
6
通讯作者：
Feng Zheng;Zhan Song;Ling Shao;R. Chung;K. Jia;Xinyu Wu
中科院分区：
文献类型：
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作者：
Feng Zheng;Zhan Song;Ling Shao;R. Chung;K. Jia;Xinyu Wu
半监督学习最近在机器学习中受到了相当大的关注。在本文中，我们提出了一种新的基于扩散映射的半监督算法的降维，可视化和数据表示。不同于以往的工作，只使用几何信息的相似性度量的建设，一个分布式的相似性度量被引入到修改样本的几何关系。该度量是使用每个样本标签上的后验概率定义的，该后验概率是通过期望最大化（EM）算法学习的。通过我们提出的方法学习的内在流形上的点之间的欧几里得距离等于原始空间中的标签相关的“扩散距离”，该距离由分布相似性相关度量修改。该算法在保持局部流形结构的基础上，将样本分为不同的类，从而有利于分类。在手写体数字、Yale人脸、UCI数据集和Weizmann数据集上的实验结果表明，该算法能够显著提高分类精度。
Semi-supervised learning has recently received considerable attention in machine learning. In this paper, we propose a novel diffusion maps based semi-supervised algorithm for dimensionality reduction, visualization and data representation. Unlike previous work which uses only geometric information for similarity metric construction, a distributional similarity metric is introduced to modify the geometric relationship of samples. This metric is defined using the posterior probability over the labels of each sample, which is learned through the Expectation–Maximization (EM) algorithm. The Euclidean distance between points on the intrinsic manifold learned by our proposed method is equal to the label-dependent “diffusion distance”, which is modified by the distributional similarity related metric, in the original space. Our algorithm preserves the local manifold structure in addition to separating samples in different classes, thus facilitates the classification. Encouraging experimental results on handwritten digits, Yale faces, UCI data sets and the Weizmann data set show that the algorithm can improve the classification accuracy significantly.