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Low-distortion embeddings are critical building blocks for developing random sampling and random projection algorithms for linear algebra problems.
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A bound on the deviation probability for sums of non-negative random variables
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Polynomial interior point cutting plane methods
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Embedding the diamond graph in L p {L}_{p} and dimension reduction in L 1 {L}_{1}
J. R. Lee and A. Naor · 2004
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On the impossibility of dimension reduction in ℓ 1 \ell_{1}
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Subgradient and sampling algorithms for ℓ 1 \ell_{1} regression
K. Clarkson · 2005
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Sampling algorithms for ℓ 2 \ell_{2} regression and applications
P. Drineas, M. W. Mahoney, and S. Muthukrishnan · 2006
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Improved approximation algorithms for large matrices via random projections
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A fast randomized algorithm for overdetermined linear least-squares regression
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Sampling algorithms and coresets for ℓ p \ell_{p} regression
A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney · 2009
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CUR matrix decompositions for improved data analysis
M. W. Mahoney and P. Drineas · 2009
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Randomized Algorithms for Matrices and Data
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LSRN: A parallel iterative solver for strongly over- or under-determined systems
X. Meng, M. A. Saunders, and M. W. Mahoney · 2011
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Subspace embeddings for the ℓ 1 \ell_{1} -norm with applications
C. Sohler and D. P. Woodruff · 2011
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Low rank approximation and regression in input sparsity time
K. L. Clarkson and D. P. Woodruff · 2012
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Fast approximation of matrix coherence and statistical leverage
P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. P. Woodruff · 2012
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