维数约简算法简述

Introduction of the Dimensionality Reduction Algorithm

机器学习是近几年研究的热点,维数约简算法是机器学习的必要手段,本文从维数约简算法的定义讲起,介绍了几种典型的数据降维算法,其中包括线性降维和非线性降维,流形学习是非线性降维的代表算法。并且介绍了每个算法的构造过程及其特点,在此基础上分析了所有维数约简算法的执行效率时间和空间复杂度,并且给出了每个算法的特点和算法的核心思想,最后在此基础上给予总结,为后面研究者提供参考和借鉴。

Machine learning,mainly realized through dimensionality reduction,has become a hot topic for research inrecent years.This paper first presents the definition of the dimensionality reduction algorithm,and then introduces severaltypical data dimensionality reduction algorithms including linear dimensionality reduction and non-linear dimensionalityreduction(manifold learning is the typical algorithm of non-linear dimensionality reduction).Besides,the paper elaborateson the construction process and characteristics of each algorithm,then analyzes the execution efficiency time and spacecomplexity of all dimensionality reduction algorithms and provides the features and key point of each algorithm.Mostimportantly,the final conclusion offers references to future researchers.