1990
DOI: 10.1002/cem.1180040111
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Comments on the NIPALS algorithm

Abstract: The Non‐linear Iterative Partial Least Squares (NIPALS) algorithm is used in principal component analysis to decompose a data matrix into score vectors and eigenvectors (loading vectors) plus a residual matrix. NIPALS starts with some guessed starting vector. The principal components obtained by NIPALS depends on the starting vector; the first principal component could not always be computed. Wold has suggested a starting vector for NIPALS, but we have found that even if this starting vector is used, the first… Show more

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Cited by 39 publications
(18 citation statements)
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“…36 Explanatory variables were rank ordered by VIP score and eliminated from the model in a stepwise progression starting with the variable having the lowest VIP score. The Q 2 values corresponding to the range of explanatory variables included in the model are shown in Figure 4A.…”
Section: Resultsmentioning
confidence: 99%
“…36 Explanatory variables were rank ordered by VIP score and eliminated from the model in a stepwise progression starting with the variable having the lowest VIP score. The Q 2 values corresponding to the range of explanatory variables included in the model are shown in Figure 4A.…”
Section: Resultsmentioning
confidence: 99%
“…The (resampled) data matrix X is decomposed by X = v 1 t T 1 + R, where v 1 denotes an estimate of the first PC of X, t 1 represents the appendent PC scores of each subject and R is the remaining residual. As an estimate for v 1 , Wold et al (1987) propose the (normalized) column of X with the largest variance, but the employment of other start vectors is possible as well (Miyashita et al (1990)). The NIPALS algorithm is iterated with R acting as new start matrix until all PCs required for further analysis are computed.…”
Section: Pca Via Non-linear Iterative Partial Least Squaresmentioning
confidence: 99%
“…In order to reduce the dimensionality of the feature space and extract principle components of image the NIPALS algorithm [2] is used. The SVM-classifier solves the problem of training and classification of images.…”
Section: Figure 1 Region Of Interest Bounded By Lines Of Browsmentioning
confidence: 99%