The impact of missing measurements on PCA and PLS prediction and monitoring applications

Nelson, Philip R.; MacGregor, John F.; Taylor, Paul A.

doi:10.1016/j.chemolab.2005.04.006

Cited by 57 publications

(33 citation statements)

References 13 publications

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“…This algorithm was based on the simplest missing data imputation method: the trimmed score imputation (TRI). The algorithm studied by Bro et al [3] and the one found in new releases of the PLS Toolbox are based on a slightly more complex imputation method: projection to the model plane (PMP) [10]. In the present paper, several ekf variants with different missing data methods, including TRI and PMP, will be studied.…”

Section: Cross-validation In Pcamentioning

confidence: 99%

Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects

Camacho

Ferrer

2014

Chemometrics and Intelligent Laboratory Systems

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ElsevierCamacho Páez, J.; Ferrer Riquelme, AJ. (2014). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical Aspects. Chemometrics and Intelligent Laboratory Systems. 131:37-50. doi:10.1016/j.chemolab.2013.12.003 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 in the main commercial software packets for chemometrics. Finally, the choice of the missing data algorithm within ekf is also investigated.

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Section: Cross-validation In Pcamentioning

confidence: 99%

Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects

Camacho

Ferrer

2014

Chemometrics and Intelligent Laboratory Systems

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“…Keywords: variable selection; chemical samples; PLS regression multivariado que relaciona as matrizes de variáveis X (independentes) e Y (dependentes). Tal regressão apresenta vantagens quando comparada à tradicional regressão linear múltipla, visto que não é afetada por variáveis altamente correlacionadas, elevados níveis de ruído e observações faltantes [14]. A regressão PLS também é recomendada em situações em que o número de variáveis é superior ao número de observações [1,15].…”

Section: Introductionunclassified

Seleção De Variáveis Para Categorização De Amostras Químicas

Anzanello

2017

Eclet. Quim. J.

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This paper presents a method to select the best variables to categorize chemical samples into two classes, say conforming or non-conforming. For that matter, PLS regression is combined with a data mining tool, the k-Nearest Neighbor classification technique, through an iterative variable selection process. The recommended subset of variables is chosen based on several criteria: sensitivity, specificity and percent of retained variables. When applied to two datasets related to wine analysis and one associated to QSAR, the proposed method significantly reduced the number of variables required for classification, while yielding superior categorization performance when compared to using all original variables.

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“…The advantage of counterpropagation model is that it can deal with missing values without any assumption about the distribution of the data. PLS can also be performed with missing values, but it is assumed that data is normally distributed with zero mean [12,13].…”

Section: Counterpropagation Neural Networkmentioning

confidence: 99%

Data exploration on standard asphalt mix analyses

Tušar

Novič

2009

Journal of Chemometrics

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aThe purpose of our study was the evaluation of the most important factors that affect the volumetric and conventional mechanical properties of produced asphalt mix and the volumetric properties of built-in asphalt layer. Asphalt mix design follows the standard procedure (Marshall procedure). We were interested not only in the quantity of bitumen specified by the Marshall procedure, but also in the quantity of stone aggregate fractions, temperatures of production and properties of bitumen that is used. The influence of these factors was investigated with several models. For the building of models we used 444 asphalt samples, analysed by one laboratory. To select the most important factors, several multiple linear regression (MLR) models, partial least squares (PLS) regression models and counterpropagation neural network models were made. Obtained models were tested with leave-one-out (LOO) and leave-10%-out cross-validation procedures. The results of MLR and PLS models show that the independent variables are closely related. Among 21 variables there is only one found as less important. MLR and PLS models show better predictive ability than counterpropagation neural network models. The best MLR models will be employed for the preparation of the asphalt mix design (recipe) with some unknown material.

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The impact of missing measurements on PCA and PLS prediction and monitoring applications

Cited by 57 publications

References 13 publications

Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects

Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects

Seleção De Variáveis Para Categorização De Amostras Químicas

Data exploration on standard asphalt mix analyses

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