Sparse random test matrices can replace dense Gaussian matrices in the farPCA low-rank approximation algorithm, preserving accuracy while reducing computation time.
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Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Sparse random test matrices can replace dense Gaussian matrices in the farPCA low-rank approximation algorithm, preserving accuracy while reducing computation time.