Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables.

PCA Example\do_cca_prediction.m
...........\do_cca_training.m
...........\eof_analysis.m
...........\eof_project.m
...........\principal_component_analysis.m
PCA Example

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