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PCA is mostly used as a tool in exploratory data analysis and for making predictive models . It's often used to visualize genetic distance and relatedness between populations. PCA can be done by eigenvalue decomposition of a data covariance (or correlation ) matrix or singular value decomposition of a data matrix , usually after mean centering (and normalizing or using Z-scores ) the data matrix for each attribute. [4] The results of a PCA are usually discussed in terms of component scores , sometimes called factor scores (the transformed variable values corresponding to a particular data point), and loadings (the weight by which each standardized original variable should be multiplied to get the component score). [5]




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