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A Comparative Study of Some Manifold Learning Algorithms

LI Xiao-li1,XUE Qing-fu2(1.Quanzhou Economic and Trade Vocational and Technical College,Quanzhou 362000,China ;2.Quanzhou Medical College,Quanzhou 362000,China)  
How to obtain the highly nonlinear low-dimensional manifolds in the high-dimensional observation space is the goal of manifold learning.Currently,most of the manifold learning algorithms are applied to the nonlinear dimensionality reduction and data visualization,such as Isomap,LLE,Laplacian Eigenmap etc.This paper analysises and compares this three manifold learning algorithms by experiments,which reveals the characteristics of manifold learning algorithms for dimensionality reduction and data analyses.
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