How can the Eigenvalues in PCA be used to determine the significance of the corresponding Eigenvectors?

  • By defining the direction of the eigenvectors
  • By indicating the mean of each eigenvector
  • By representing the amount of variance captured
  • By showing the noise in the data
In PCA, eigenvalues are used to determine the significance of the corresponding eigenvectors by representing the amount of variance captured by each component. The larger the eigenvalue, the more significant the component.
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