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Principal component analysis is a technique to decompose an array of numerical data into a set of orthogonal vectors (uncorrelated linear combinations of the variables) called principal components. Few first principal components are often suffice to grasp nearly all multivariate variability of the data; therefore PCA is one of the data reduction / dimensionality reduction methods.