Randomized subspace iteration improves low-rank approximation quality over randomized SVD for pretrained models by using power iterations to enhance spectral separation, preserving predictive accuracy better under aggressive compression.
Saibaba , Randomized subspace iteration: Analysis of canonical angles and unitarily invariant norms , SIAM Journal on Matrix Analysis and Applications, 40 (2019), pp
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Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
Randomized subspace iteration improves low-rank approximation quality over randomized SVD for pretrained models by using power iterations to enhance spectral separation, preserving predictive accuracy better under aggressive compression.