… for generalized … principal component analysis (GPCA), which, upon closer inspection, turns
out to be a nested descriptor. The corresponding backward nested descriptor analysis (BNDA…

… , the generalized principal component analysis (GPCA), which … GPCA are the fault data
set and the normal operating data set respectively, then the generalized principal components …

… In this case, dimension reduction is an effective method to process these data, and the most
classical method is Principal Component Analysis (PCA). How to apply the PCA dimension …

… These projections recognize the correlations across each image dimension, and are
pursued using the generalized principal component analysis (GPCA). It is shown, through the …

… Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

… such as principal components analysis give results that are unstable and difficult to interpret.
Here, we present adaptive generalized principal components analysis (adaptive gPCA), a …

… The meaning of features identified through principal component analysis is … principal
component analysis, with each step illustrated by simulated spectra. Principal component analysis …

… As we will see below, this formulation is the “correct” generalization of many of the regularized
… [7], itself a special case of twoway sparse GPCA with the generalizing operators Q, R both …

… GPCA is more coherent with dominant strategies in intrusion detection. We illustrate the
application of GPCA … With GPCA, we can identify anomalies in the data stream following a …

… chapter as Generalized Principal Component Analysis. The … need to use Generalized Principal
Component Analysis (GPCA). … around the multivariate technique GPCA. The reviews also …