



PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.8, 0.5) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the corresponding eigenvalue, and shifted so their tails are at the mean.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 pircing of values of linearly uncorrelated variables called principal components (or sometimes, principal modes of variation).
The number of principal components is less than or equal to the smaller of (number of original variables or number of observations).
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