引用本文:李正欣,郭建胜,惠晓滨,宋飞飞.基于共同主成分的多元时间序列降维方法[J].控制与决策,2013,28(4):531-536
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】 附件
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 344次   下载 1512 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于共同主成分的多元时间序列降维方法
李正欣, 郭建胜, 惠晓滨, 宋飞飞
空军工程大学 装备管理与安全工程学院
摘要:

针对常见的降维方法难以有效保留多元时间序列主要特征的问题, 分析了传统主成分分析(PCA) 方法在多
元时间序列降维中的局限性, 提出一种基于共同主成分分析的多元时间序列降维方法, 并通过仿真实验比较了两种
方法的降维有效性和计算复杂度. 实验结果表明, 所提出的降维方法能够以相对较小的计算代价, 更有效地对多元时
间序列进行降维.

关键词:  

降维  多元时间序列  主成分分析  共同主成分分析  计算复杂度

DOI:
分类号:
基金项目:
Dimension reduction method for multivariate time series based on common principal component
LI Zheng-xin, GUO Jian-sheng, HUI Xiao-bin, SONG Fei-fei
Abstract:

Existing dimension reduction method for multivariate time series can’t preserve their feature effectively.
Therefore, the drawback of PCA method is analyzed, when it is used in MTS dimension reduction, and based on common
principal component analysis, a dimension reduction method for multivariate time series is proposed. The computational
complexity and the validity of dimension reduction are compared between different methods. The results of experiments
show that the proposed method can reduce dimension effectively at comparatively low computational cost, and at the same
time preserve most feature of multivariate time series.

Key words:  

dimension reduction  multivariate time series  principal component analysis  common principal component analysis  computational complexity

用微信扫一扫

用微信扫一扫