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first and points to the next highest variance direction, and so on. Eigenvalues: Quantifying Significance Each eigenvector has a corresponding eigenvalue. An eigenvalue in PCA quantifies the amount of variance captured in the direction of its eigenvector. A higher eigenvalue means more variance