Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. PCA is used for dimensionality reduction, feature extraction, and data visualization, especially when dealing with high-dimensional data.
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