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We study (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression.
Solving least squares problems
C. L. Lawson and R. J. Hanson · 1974
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Linear regression analysis
G.A.F. Seber and A.J. Lee · 1977
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Time Series Analysis
J. D. Hamilton · 1994
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Numerical Methods for Least Squares Problems
A. Björck · 1996
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Predicting multivariate responses in multiple linear regression
L. Breiman and J. Friedman · 1997
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
N. Cristianini and J. Shawe-Taylor · 2000
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An introduction to variable and feature selection
I. Guyon and A. Elisseeff · 2003
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Matrix Mathematics: Theory, Facts, and Formulas with Applications to Linear Systems Theory
D. S. Bernstein · 2005
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An interior point newton-like method for non-negative least squares problems with degenerate solution
S. Bellavia, M. Macconi, and B. Morini · 2006
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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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Solutions and optimality criteria to box constrained nonconvex minimization problems
D.Y. Gao · 2007
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Sampling from large matrices: An approach through geometric functional analysis
M. Rudelson and R. Vershynin · 2007
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Relative-error CUR matrix decompositions
P. Drineas, M. W. Mahoney, and S. Muthukrishnan · 2008
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Twice-ramanujan sparsifiers
J.D. Batson, D.A. Spielman, and N. Srivastava · 2009
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Random projections for the nonnegative least-squares problem
C. Boutsidis and P. Drineas · 2009
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Topics in Matrix Sampling Algorithms
C. Boutsidis · 2011
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Near-optimal column based matrix reconstruction
C. Boutsidis, P. Drineas, and M. Magdon-Ismail · 2011
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Nimble Algorithms for Cloud Computing
R. Kannan and S. Vempala · 2013
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