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For questions involving calculation or interpretation of eigenvalues or eigenvectors.

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How to derive the three matrices of SVD from eigenvalue decomposition in Kernel PCA?

Based on @whuber's answer it's simply: $$ \mathbf{V}=\mathbf{X^TU\Sigma^{-1}} $$
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How to derive the three matrices of SVD from eigenvalue decomposition in Kernel PCA?

Kernel PCA is usually done via eigenvalue decomposition of the Kernel Matrix $\mathbf{K}$ and standard PCA via SVD of the input $\mathbf{X}$. In standard PCA as far as I know we can derive $\mathbf{S …
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