Degrees of freedom estimation in Principal Component Analysis and Consensus Principal Component Analysis
作者: Sahar HassaniHarald MartensEl Mostafa QannariAchim Kohler
作者单位: 1Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, P.O.BOX 210,1431 Ås, Norway
2Centre for Integrative Genetics (CIGENE), Department of Mathematical Sciences and Technology (IMT), Norwegian University of Life Sciences, 1432 Ås, Norway
3UNAM University, ONIRIS, USC "Sensometrics and Chemometrics Laboratory", Nantes, F-44322, France
4INRA, Nantes, F-44316, France
刊名: Chemometrics and Intelligent Laboratory Systems, 2012, Vol.118 , pp.246-259
来源数据库: Elsevier Journal
DOI: 10.1016/j.chemolab.2012.05.015
关键词: Principal Component Analysis (PCA)Consensus Principal Component Analysis (CPCA)Cross-validation (CV)Degree of freedom (DF)
原始语种摘要: Abstract(#br)The concept of degree of freedom (DF) is an important issue in statistical model assessment and parameter estimation. In this paper, we investigate this concept within the context of data modeling by Principal Component Analysis (PCA) and its multi-block extension, the Consensus Principal Component Analysis (CPCA). We run simulation studies and assess the degrees of freedom by comparing cross-validated error estimates with error estimates from uncorrected model fits. These simulation studies reveal that the DF consumption in PCA and CPCA depends on the eigenvalue structure of the data at hand. We also show that the obtained DF estimates can be used to obtain realistic error estimations without performing cross-validation. Furthermore, it is shown how different strategies of...
全文获取路径: Elsevier  (合作)
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