Abstract: Global preserving manifold learning algorithms are mainly based on preserving global geometric properties between high-dimensional observed space and intrinsic low-dimensional manifold.This paper compares in detail the characters and interrelations of several classical manifold learning algorithms based on preserving global properties.Some advantages and shortcomings of these algorithms are shown.Experimental results demonstrate the differences of these algorithms about intrinsic dimension and intrinsic low-dimensional manifold.Finally，several new research directions of manifold learning are proposed.

Key words: global preserving, spectral method, kernel Principal Components Analysis, ISOmetric MAPping, Maximum Variance Unfolding

摘要： 全局保持的流形学习算法主要是基于保持高维观测空间和内在低维流形的全局几何特性。详细比较了全局保持的典型流形学习算法的特点及其相互之间的联系，标明了它们的优点与缺陷。实验说明这些方法发现的内在维数和内在低维流形的差异。最后提出了一些新的流形学习研究方向。

关键词: 全局保持, 谱方法, 核主成分分析, 等度规映射, 最大方差展开