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\section{ %\section{  Principle Component Analysis (PCA)} \textit{Definition:} %\textit{Definition:}  constructs a new matrix of a dataset that indicates the change of two data points with respect to each other (covariance matrix). It then finds the eigenvalues and eigenvectors (“principle directions”) of the matrix. The difference between two data sets is the sum of the difference between the eigenvalues Note: %Note:  may only be able to derive a small number of eigenvectors (depends on the complexity of the data) http://en.wikipedia.org/wiki/Covariance  {\it %{\it  References:} Heyer \& Schloerb 1997, Brunt \& Heyer 2001, Yeremi et al. 2014