关键词: 高维数据, 降维, 流形学习, 局部邻域, 本征维度, 局部切方向

Abstract: 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.

Key words: high-dimensional data, dimensionality reduction, manifold learning, local neighborhood, intrinsic dimensionality, local tangent orientation