How does the concept of orthogonality play into PCA?
- It ensures that the principal components are uncorrelated
- It guarantees the uniqueness of the solution
- It helps in the calculation of eigenvalues
- It is essential for dimensionality reduction
Orthogonality ensures that the principal components are uncorrelated. PCA aims to find orthogonal directions (principal components) in the feature space along which the original data varies the most. These orthogonal components represent independent linear effects present in the data.
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