Local Tangent Space Discriminant Analysis

摘要

We propose a novel supervised dimensionality reduction method named local tangent space discriminant analysis (TSD) which is capable of utilizing the geometrical information from tangent spaces. The proposed method aims to seek an embedding space where the local manifold structure of the data belonging to the same class is preserved as much as possible, and the marginal data points with different class labels are better separated. Moreover, TSD has an analytic form of the solution and can be naturally extended to non-linear dimensionality reduction through the kernel trick. Experimental results on multiple real-world data sets demonstrate the effectiveness of the proposed method.