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Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects

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Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects

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Vitale, R.; Westerhuis, JA.; Naes, T.; Smilde, AK.; De Noord, OE.; Ferrer, A. (2017). Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects. Journal of Chemometrics. 31(12):1-15. doi:10.1002/cem.2937

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/108101

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Title: Selecting the number of factors in principal component analysis by permutation testing Numerical and practical aspects
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
[EN] Selecting the correct number of factors in principal component analysis (PCA) is a critical step to achieve a reasonable datamodelling,where the optimal strategy strictly depends on the objective PCA is applied for. ...[+]
Subjects: Deflation , Eigenvalues , Permutation testing , Principal component analysis (PCA) , Projection
Copyrigths: Reserva de todos los derechos
Source:
Journal of Chemometrics. (issn: 0886-9383 )
DOI: 10.1002/cem.2937
Publisher:
John Wiley & Sons
Publisher version: https://doi.org/10.1002/cem.2937
Thanks:
Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2014-55276-C5-1R; Shell Global Solutions International B.V.
Type: Artículo

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