Nyström's method always yields higher-accuracy leading eigenvalues than Rayleigh-Ritz for positive semi-definite matrices given a subspace approximation, with improvements that can be arbitrarily large.
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Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
Conditioning on a small number of carefully designed linear combinations of data enables machine-precision accurate Gaussian process predictions at low cost for large-scale and online problems.
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.
citing papers explorer
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Finding accurate eigenvalues and eigenvectors of positive semi-definite matrices given a subspace
Nyström's method always yields higher-accuracy leading eigenvalues than Rayleigh-Ritz for positive semi-definite matrices given a subspace approximation, with improvements that can be arbitrarily large.
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Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
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Fast and accurate conditioning for large-scale and online Gaussian process prediction problems
Conditioning on a small number of carefully designed linear combinations of data enables machine-precision accurate Gaussian process predictions at low cost for large-scale and online problems.
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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.