PCA model building with missing data: New proposals and a comparative study

[EN] This paper introduces new methods for building principal component analysis (PCA) models with missing data: projection to the model plane (PMP), known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR). These methods are adapted from their PCA model exploitation version to deal with the more general problem of PCA model building when the training set has missing values. A comparative study is carried out comparing these new methods with the standard ones, such as the modified nonlinear iterative partial least squares (NIPALS), the it- erative algorithm (IA), the data augmentation method (DA) and the nonlinear programming approach (NLP). The performance is assessed using the mean squared prediction error of the reconstructed matrix and the cosines between the actual principal components and the ones extracted by each method. Four data sets, two simulated and two real ones, with several percentages of missing data, are used to perform the comparison. Guardar / Salir Siguiente >Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and the Spanish Ministry of Economy and Competitiveness through grant ECO2013-43353-R. The authors gratefully acknowledge Salvador Garcia-Munoz for providing the Phi toolbox (version 1.7) to perform the nonlinear programming approach (NLP) method.Folch-Fortuny, A.; Arteaga Moreno, FJ.; Ferrer Riquelme, AJ. (2015). PCA model building with missing data: New proposals and a comparative study. Chemometrics and Intelligent Laboratory Systems. 146:77-88. https://doi.org/10.1016/j.chemolab.2015.05.006S778814