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Principal Components: what do they mean?

Question asked by on Oct 7, 2015
Latest reply on Oct 14, 2015 by

Hi -


I understand that principle components are calculated such that the first explains the greatest amount of variation in a dataset, the second PC explains the greatest amount of remaining variation, and so on. I understand PCs are uncorrelated variables that retain information in the original dataset using reduced dimensions.


What I'm unclear on is what PCs mean in terms of the multiband raster output from the Principal Components tool. I can look at my Eigenvalues for each PC and determine how many to use to maintain the variation in my dataset, but I don't know how to interpret the PCs in raster form.


For example, here is PC1, with values ranging from 4.6 (red) to 0 (blue). What do these values mean? My best guess is that these values are 'distances' or errors from the PC1 axis.