.png){kind=icon}
返回
Robust factor analysis for compositional data
.png){kind=icon}
OA
AI
DOI：10.1016/j.cageo.2008.12.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Factor analysis as a dimension reduction technique is widely used with compositional data. Using the method for raw data or for improperly transformed data will, however, lead to biased results and consequently to misleading interpretations. Although some procedures, suitable for factor analysis with compositional data, were already developed, they require pre-knowledge of variable groups, or are complicated to handle. We present an approach based on the centred logratio (clr) transformation that does not build on this pre-knowledge, but still recognizes the specific character of compositional data. In addition, by using the isometric logratio transformation it is possible to robustify factor analysis using a robust estimation of the covariance matrix. A back-transformation of the results to the clr space allows an interpretation of the results with compositional biplots. The method is demonstrated with data from the Kola project, a large ecogeochemical mapping project in northern Europe. (c) 2009 Elsevier Ltd. All rights reserved.
Keyword:
Centred logratio transformation
Isometric logratio transformation
Factor analysis
Compositional biplot
Robust estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.4
论文数:
5.0K
被引数:
1.5W
机构
查看更多机构
引用论文
Factor analysis applied to regional geochemical data: problems and possibilities.png)
APPLIED GEOCHEMISTRY
APPLIED GEOCHEMISTRYIF3.4
PREAI
分享收藏Effects of grain boundaries on performance and hot-carrier reliability of excimer-laser annealed polycrystalline silicon thin film transistors晶界对准分子激光退火多晶硅薄膜晶体管性能和热载流子可靠性的影响
PREAI
分享收藏 没有更多内容