主成分分析与奇异值分解技术在铁路数据预处理中的应用  

Principal component analysis and singular value decomposition technologies in railway data preprocessing

作　　者：徐贵红[1] 郭剑峰[1] 杨涛存[1] 东春昭[1] XU Guihong;GUO Jianfeng;YANG Taocun;DONG Chunzhao(Research and Application Innovation Center for Big Data Technology in Railway, China Academy of Railway Sciences, Beijing 100081, China)

机构地区：[1]中国铁道科学研究院铁路大数据研究与应用创新中心,北京100081

出　　处：《铁路计算机应用》2016年第9期55-57,62,共4页Railway Computer Application

基　　金：中国铁路总公司科技研究开发计划课题(2015X003-F);中国铁道科学研究院院基金重大项目(1551DZ8004)

摘　　要：数据预处理是在数据建模之前对采集到的原始数据进行的一些前期处理工作,能够滤除原始数据存在的噪声干扰、降低数据维度进而提取数据的时域特征。铁路运输行业在生产过程中累积的大量数据往往包含着噪声干扰,并且经常是海量高维的,无法直接用于数据建模、分析和挖掘。主成分分析与奇异值分解作为线性代数中一种重要的矩阵分解技术,已经成为近年来常用的数据时域预处理方法,本文主要论述主成分分析与奇异值分解技术在铁路数据预处理中的应用。Data preprocessing is a preliminary work before data modeling. It can filter the noise interference of original data and can reduce data dimension to extract features of the data in time domain. The large amount of data accumulated in the production process of railway transport industry often contains noise interference. Besides, it is often massive and high dimensional, which cannot be directly used for data modeling, analysis and mining. Principal component analysis and singular value decomposition are important matrix decomposition technologies in linear algebra. They have been commonly used in data time-domain preprocessing in recent years. This paper mainly discussed the use of principal component analysis and singular value decomposition technologies in railway data preprocessing of the application.

关 键 词：主成分分析 奇异值分解 数据预处理 

分 类 号：U29-39[交通运输工程—交通运输规划与管理]