What is the relationship between the eigenvalue of a component and the variance of that component in PCA?
- It depends on the dataset
- There is no relationship
- They are directly proportional
- They are inversely proportional
The eigenvalue of a component in PCA is directly proportional to the variance of that component. In other words, a larger eigenvalue corresponds to a larger amount of variance explained by that principal component.
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