流形学习中非线性维数约简方法概述

Overview of nonlinear dimensionality reduction methods in manifold learning

较为详细地回顾了流形学习中非线性维数约简方法,分析了它们各自的优势和不足。与传统的线性维数约简方法相比较,可以发现非线性高维数据的本质维数,有利于进行维数约简和数据分析。最后展望了流形学习中非线性维数方法的未来研究方向,期望进一步拓展流形学习的应用领域。

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.