Improvement method of dimensionality reduction through mining density information

Diffusion maps is based on manifold learning nonlinear dimensionality reduction method. The effective of the diffusion maps dimension reduction is affected by the distribution of the neighboring points,mining local density information,it can better maintain the structure of the data. Using sample points after clustering to construct density coefficient,this paper proposed an improved method of diffusion maps algorithm,effective to keep the high dimension data of the manifold structure,and confirmed the proposed method in the experiments.