Asked about Norms, projections and decompositions
What does SVD give you?
An answer that works
A decomposition of any matrix into rotations and a scaling. Truncating to the largest singular values gives the best low-rank approximation, which underlies compression, PCA and parameter-efficient methods like LoRA.
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