After applying PCA to your dataset, you find that some Eigenvectors have very small corresponding Eigenvalues. What does this indicate, and what action might you take?
- This indicates a problem with the data and you must discard it
- This indicates that these eigenvectors capture little variance, and you may choose to discard them
- This is an indication that PCA is not suitable for your data
- This means that you must include these eigenvectors
Very small eigenvalues indicate that the corresponding eigenvectors capture little variance, and discarding them would reduce dimensions without losing much meaningful information.
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