Presents provably accurate compressive sensing algorithms for one-pass sparse approximation of top eigenvectors of huge approximately low-rank matrices with sublinear runtime.
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A matrix-sketching framework with sensitivity-informed ensemble sampling selects data subsets that preserve the conditioning of the full-data Fisher Information Matrix for inverse problems.
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Fast One-Pass Sparse Approximation of the Top Eigenvectors of Huge Approximately Low-Rank Matrices? Yes, $MAM^*$!
Presents provably accurate compressive sensing algorithms for one-pass sparse approximation of top eigenvectors of huge approximately low-rank matrices with sublinear runtime.
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Sensitivity-preserving of Fisher Information Matrix through random data down-sampling for experimental design
A matrix-sketching framework with sensitivity-informed ensemble sampling selects data subsets that preserve the conditioning of the full-data Fisher Information Matrix for inverse problems.