Local manifold learning with robust neighbors selection for hyperspectral dimensionality reduction
Local manifold learning with robust neighbors selection for hyperspectral dimensionality reduction
复制标题
DOI：
10.1109/igarss.2016.7729001
复制
发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
D. Hong;N. Yokoya;Xiaoxiang Zhu
中科院分区：
文献类型：
--
作者：
D. Hong;N. Yokoya;Xiaoxiang Zhu
流形学习已经成功地应用于高光谱降维，在数据中嵌入非线性和非凸流形。然而，流形学习降维对非均匀数据分布和邻域选择敏感。为了在一定程度上解决这两个问题，在这项工作中，一个新的流形框架的基础上局部线性嵌入（LLE），即局部归一化和局部特征选择（LNLFS），提出。分类探索作为一个潜在的应用程序来验证所提出的算法。使用不同的降维方法获得的数据的分类精度进行评估和比较，同时应用两种策略选择的训练和测试样本：随机抽样和基于区域的抽样。实验结果表明，LNLFS得到的分类精度是上级国家的最先进的降维方法。
Manifold learning has been successfully applied to hyperspectral dimensionality reduction to embed nonlinear and nonconvex manifolds in the data. However, dimensionality reduction by manifold learning is sensitive to non-uniform data distribution and the selection of neighbors. To address the two issues to some extents, in this work a new manifold framework based on locality linear embedding (LLE), namely local normalization and local feature selection (LNLFS), is proposed. Classification is explored as a potential application to validate the proposed algorithm. Classification accuracy using data obtained using different dimensionality reduction methods is evaluated and compared, while applying two kinds of strategies for selecting the training and test samples: random sampling and region-based sampling. Experimental results show the classification accuracy obtained with LNLFS is superior to state-of-the-art dimensionality reduction methods.