How is the amount of variance explained related to Eigenvalues in PCA?
- Eigenvalues are unrelated to variance
- Eigenvalues represent the mean of the data
- Larger eigenvalues explain more variance
- Smaller eigenvalues explain more variance
In PCA, the amount of variance explained by each principal component is directly related to its corresponding eigenvalue. Larger eigenvalues mean that more variance is explained by that particular component.
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