Overview of nonlinear dimensionality reduction methods in manifold learning

A detailed retrospection was made on nonlinear dimensionality reduction methods in manifold learning,whose advantages and defects were pointed out respectively.Compared with traditional linear method,nonlinear dimensionality reduction methods in manifold learning could discover the intrinsic dimensions of nonlinear high-dimensional data effectively,help researcher to reduce dimensionality and analyzer data better.Finally,the prospect of nonlinear dimensionality reduction methods in manifold learning was discussed,so as to extend the application area of manifold learning.