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Conversion of a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. Wikipedia