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

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。

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

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.