Principal Component Analysis (PCA) is an extremely useful technique for reducing multi-band raster data to a more manageable number of independent bands. Often this is simply a matter of calculating the Eigen Values to determine which of the first few Principal Components (PCs) represent the majority of the information and just keeping the most important PC bands. But sometimes you want to know which of the original bands contributed the most to those important PCs. Calculating the Factor Loadings helps you make this determination.
Quickly convert a Table with n rows to a n x n Matrix where each column contains the original Table values