If not, then does SVD always work better than diagonalization? It seems to me that for square images, diagonalization is the more straightforward method when compressing images in MATLAB. However, I want to know if SVD has an advantage that I am unaware of.
2026-03-25 01:16:24.1774401384
For square images, do SVD and diagonalization always produce the same result in compressing images?
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Intuitively SVM generates orthogonal features from the image. The eigenvectors are not necessarily orthogonal. At a given cut in the number features for reconstructing back the image, it is more useful to have vectors that are different from each other, and orthogonal is as different as you can get