Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/56415
Type: Artigo de periódico
Title: Computational performance and cross-validation error precision of five PLS algorithms using designed and real data sets
Author: Martins, JPA
Teofilo, RF
Ferreira, MMC
Abstract: An evaluation of computational performance and precision regarding the cross-validation error of five partial least squares (PLS) algorithms (NIPALS, modified NIPALS, Kernel, SIMPLS and bidiagonal PLS), available and widely used in the literature, is presented. When dealing with large data sets, computational time is an important issue, mainly in cross-validation and variable selection. In the present paper, the PLS algorithms are compared in terms of the run time and the relative error in the precision obtained when performing leave-one-out cross-validation using simulated and real data sets. The simulated data sets were investigated through factorial and Latin square experimental designs. The evaluations were based on the number of rows, the number of columns and the number of latent variables. With respect to their performance, the results for both simulated and real data sets have shown that the differences in run time are statistically different. PIS bidiagonal is the fastest algorithm, followed by Kernel and SIMPLS. Regarding cross-validation error, all algorithms showed similar results. However, in some situations as, for example, when many latent variables were in question, discrepancies were observed, especially with respect to SIMPLS. Copyright (C) 2010 John Wiley & Sons, Ltd.
Subject: computational performance
partial least squares
experimental design
algorithms
Country: Inglaterra
Editor: John Wiley & Sons Ltd
Rights: fechado
Identifier DOI: 10.1002/cem.1309
Date Issue: 2010
Appears in Collections:Unicamp - Artigos e Outros Documentos

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