… In this study, we modified two commonly used feature extraction methods, PCA and PLS,
in order to deal with the large intragroup variations caused by experimental changes from …

… PCA is to reduce the dimensionality of large datasets (those including all the values of spectral
variables in a wide … the case of the Raman data set, and 82%, for the FT-IR one [19]. The …

… PLS-DA in comparison with PCA for different underlying data … and omics data sets are
characterized by large volume, large … a synthetic data set for which the PC chosen by PCA points …

… of the PCA exploration of high dimensional datasets, such as … If we consider a data set
obtained from a single population, it … PLS regression coefficients. This can be referred to as the …

… of Big Data and personalized medicine. In the first section, we explain the mathematical formula
behind PCA… PCA and its variants (PLS, CVA, MLM) are largely used in studies aiming to …

… carry out principal component analysis (PCA) on incomplete data set. It explains why its …
very close to those for d = 5%. Moreover, it was decided not to include missing data rates larger …

… (PLS-DA) has gained wide acceptance and huge … PLS-DA works in a similar way to PCA
to project HD data into a series of linear subspaces of the explanatory variables. However, PLS …

… PCA to a real dataset of bean pictures, and in the latter, we illustrate the results of applying
PCA to datasets … that some datasets need many PCA components to achieve large variance …

… that describe major trends of variations in the calibration data … (from all 50 spectra) of the test
data set matched correctly to the … the constructed PLS-LDA model were always very close to …

… the simulated data, in particular the close agreement between the results of PLS-DA and
PCA-DP… In all the cases studied, the PLS-DA performed better than the PCA-LDA. This occurred …