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Randomized algorithms for very large matrix problems have received a great deal of attention in recent years.
Equation of state calculations by fast computing machines
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Matrix Computations
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Algorithm 782: Codes for rank-revealing QR factorizations of dense matrices
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Bounds on singular values revealed by QR factorizations
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Singular value decomposition for genome-wide expression data processing and modeling
O. Alter, P.O. Brown, and D. Botstein · 2000
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A global geometric framework for nonlinear dimensionality reduction
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Nonlinear dimensionality reduction by local linear embedding
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Latent semantic indexing: a probabilistic analysis
C.H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala · 2000
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Regression Analysis by Example
S. Chatterjee, A.S. Hadi, and B. Price · 2000
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On the existence and computation of rank-revealing LU factorizations
C.-T. Pan · 2000
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On the distribution of the largest eigenvalue in principal components analysis
I.M. Johnstone · 2001
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Parameterisation of galaxy spectra in the 2dF galaxy redshift survey
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Wavelet-based numerical homogenization with applications
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Random projection in dimensionality reduction: applications to image and text data
E. Bingham and H. Mannila · 2001
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Using the Nyström method to speed up kernel machines
C.K.I. Williams and M. Seeger · 2001
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The maximum-volume concept in approximation by low-rank matrices
S.A. Goreinov and E.E. Tyrtyshnikov · 2001
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Spectra of “real-world” graphs: Beyond the semicircle law
I. J. Farkas, I. Derényi, A.-L. Barabási, and T. Vicsek · 2001
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Spectra and eigenvectors of scale-free networks
K.-I. Goh, B. Kahng, and D. Kim · 2001
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Vector algebra in the analysis of genome-wide expression data
F.G. Kuruvilla, P.J. Park, and S.L. Schreiber · 2002
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Massive datasets in astronomy
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Selection of genetic markers for association analyses, using linkage disequilibrium and haplotypes
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The International HapMap Project
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Singular value decomposition and principal component analysis
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Construction and arithmetics of H-matrices
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An elementary proof of a theorem of Johnson and Lindenstrauss
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Improved Nyström low-rank approximation and error analysis
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Adaptive beamforming using fast low-rank covariance matrix approximations
D.N. Spendley and P.J. Wolfe · 2008
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On sparse representations of linear operators and the approximation of matrix products
M.-A. Belabbas and P. J. Wolfe · 2008
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Relative-error CUR matrix decompositions
P. Drineas, M.W. Mahoney, and S. Muthukrishnan · 2008
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An improved approximation algorithm for the column subset selection problem
C. Boutsidis, M.W. Mahoney, and P. Drineas · 2008
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Graph sparsification by effective resistances
D.A. Spielman and N. Srivastava · 2008
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
D. Achlioptas · 2003
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Experiments with random projections for machine learning
D. Fradkin and D. Madigan · 2003
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Random projection for high dimensional data clustering: a cluster ensemble approach
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Spectra of complex networks
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Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation
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A fast randomized algorithm for the approximation of matrices
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Spectra of sparse random matrices
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On the spectrum of the normalized graph Laplacian
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M. W. Mahoney, L.-H. Lim, and G. E. Carlsson · 2008
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CUR matrix decompositions for improved data analysis
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On selecting a maximum volume sub-matrix of a matrix and related problems
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On sampling-based approximate spectral decomposition
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Sampling techniques for the Nyström method
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Spectral methods in machine learning and new strategies for very large datasets
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On landmark selection and sampling in high-dimensional data analysis
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Exact matrix completion via convex optimization
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Numerical linear algebra in the streaming model
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A fast and efficient algorithm for low-rank approximation of a matrix
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A randomized algorithm for principal component analysis
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Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities
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Graph spectra as a systematic tool in computational biology
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