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- Q2845196 subject Q7035969.
- Q2845196 subject Q8425180.
- Q2845196 abstract "In statistics, the generalized canonical correlation analysis (gCCA), is a way of making sense of cross-correlation matrices between the sets of random variables when there are more than two sets. While a conventional CCA generalizes principal component analysis (PCA) to two sets of random variables, a gCCA generalizes PCA to more than two sets of random variables. The canonical variables represent those common factors that can be found by a large PCA of all of the transformed random variables after each set underwent its own PCA.".
- Q2845196 wikiPageExternalLink factominer.free.fr.
- Q2845196 wikiPageExternalLink S1053811912001644.
- Q2845196 wikiPageWikiLink Q10861030.
- Q2845196 wikiPageWikiLink Q115542.
- Q2845196 wikiPageWikiLink Q12483.
- Q2845196 wikiPageWikiLink Q1302587.
- Q2845196 wikiPageWikiLink Q206904.
- Q2845196 wikiPageWikiLink Q2873.
- Q2845196 wikiPageWikiLink Q5709282.
- Q2845196 wikiPageWikiLink Q7035969.
- Q2845196 wikiPageWikiLink Q8425180.
- Q2845196 comment "In statistics, the generalized canonical correlation analysis (gCCA), is a way of making sense of cross-correlation matrices between the sets of random variables when there are more than two sets. While a conventional CCA generalizes principal component analysis (PCA) to two sets of random variables, a gCCA generalizes PCA to more than two sets of random variables.".
- Q2845196 label "Generalized canonical correlation".