几种流形学习算法的比较研究

A Comparative Study of Some Manifold Learning Algorithms

如何发现高维数据空间流形中有意义的低维嵌入信息是流形学习的主要目的。目前,大部分流形学习算法都是用于非线性维数约简或是数据可视化的,如等距映射(Isomap),局部线性嵌入算法(LLE),拉普拉斯特征映射算法(laplacian Eigenmap)等等,文章对这三种流形学习算法进行实验分析与比较,目的在于了解这几种流形学习算法的特点,以便更好地进行数据的降维与分析。

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.