自适应邻域选择的数据可分性降维方法

针对现有基于流形学习的降维方法对局部邻域大小选择的敏感性，且降至低维后的数据不具有很好的可分性，提出一种自适应邻域选择的数据可分性降维方法。该方法通过估计数据的本征维度和局部切方向来自适应地选择每一样本点的邻域大小；同时，使用映射数据时的聚类信息来汇聚相似的样本点，保证降维后的数据具有良好的可分性，使之实现更好的降维效果。实验结果表明，在人工生成的数据集上，新方法获得了较好的嵌入结果；并且在人脸的可视化分类和图像检索中得到了期望的结果。

Dimensionality reduction method with data separability based on adaptive neighborhood selection

The existing dimensionality reduction methods based on manifold learning are sensitive to the selection of local neighbors,and the reduced data do not have good separability.This paper proposed a dimensionality reduction method with data separability based on adaptive neighborhood selection,which adaptively selected the neighborhood at each sample point based on estimated intrinsic dimensionality of data and local tangent orientation.Meanwhile,it clustered the similar sample points by using clustering information when mapping data,which guaranteed good separability for the reduced data and achieved better dimensionality reduction results.The experimental results show that the new method derives a better embedding result on the artificially generated data sets.In addition,it can get expected result on face visualization classification and image retrieval.