Partial least squares improvement and research principal component regression extraction methods

被引：0

作者：

Xiong, Wangping ^{[1
]}

Du, Jianqiang ^{[1
]}

Nie, Wang ^{[1
]}

机构：

[1] JiangXi Univ Tradit Chinese Med, Nanchang 330004, Jiangxi, Peoples R China

来源：

2014 IEEE 11TH INTL CONF ON UBIQUITOUS INTELLIGENCE AND COMPUTING AND 2014 IEEE 11TH INTL CONF ON AUTONOMIC AND TRUSTED COMPUTING AND 2014 IEEE 14TH INTL CONF ON SCALABLE COMPUTING AND COMMUNICATIONS AND ITS ASSOCIATED WORKSHOPS | 2014年

基金：

中国国家自然科学基金;

关键词：

Partial Least Squares; Cross Validation; Interpretation of numerical; Variable Importance;

D O I：

10.1109/UIC-ATC-ScalCom.2014.27

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Partial least squares algorithm as a new type of multivariate data analysis methods, the number of cross-validation to determine the effect of poor primary component. In this paper, cross-validation, based on the interpretation of the degree of integration of the main components of the independent variables and the dependent variable, the importance of the variables as a test indicators, the data on the efficacy indexes were calculated, compared with more conventional partial least squares consistent with the theoretical value, indicating that this algorithm can solve the partial least squares method to determine the main issues to score.

引用

页码：583 / 585

页数：3

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