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Illustration on the working principle of the PCA projection. (A) Two-dimensional linear manifold embedded in a high dimensional space. (B) Eigenspectrum of a covariance matrix of the data, as the manifold is two-dimensional a clear gap appears after the second eigenvalues (blue line); a more typical eigenspectrum is shown in orange. (C) Low-dimensional representation of the data obtained through PCA.
