基于保距与保拓扑的流形学习算法

Manifold Learning Methods Based on Distance and Topology Preservation

流形学习是一种新的数据降维方法,能揭示数据的内在变化规律,其目标是发现嵌入在高维数据空间中的低维流形结构,并给出一个有效的低维表示。介绍了流形学习的基本思想,介绍了基于保距和保拓扑的一些流形学习算法,分析了这些算法的优缺点,并提出了有待进一步研究的问题。

Manifold learning is a new data dimensional reduction method,it can reveal the inherent discipline,the objective is to discover the low-dimensional manifold structure embedded in high dimensional data space,and give an effective low-dimensional formulating.The current manifold learning method gets more and more attentions in the pattern recognition and machine learning fields for its outstanding data reduction and visualization capabilities.This article describes the basic idea of manifold learning and introduced manifold learning algorithms based on distant and topological preserved,analyzes the advantages and disadvantages of these algorithms,and proposes some problems to be researched.