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We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified large-data linear model.
Distribution of eigenvalues for some sets of random matrices
Vladimir A Marchenko and Leonid A Pastur · 1967
Earlier work this paper cites.
Free random variables
Dan V Voiculescu, Ken J Dykema, and Alexandru Nica · 1992
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An Introduction to Multivariate Statistical Analysis
Theodore W Anderson · 2003
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Antonio M Tulino and Sergio Verdú · 2004
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The random projection method , volume 65
Santosh S Vempala · 2005
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Petros Drineas, Michael W Mahoney, and S Muthukrishnan · 2006
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Fumio Hiai and Dénes Petz · 2006
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Earlier work this paper cites.
An Introduction to Random Matrices
Greg W Anderson, Alice Guionnet, and Ofer Zeitouni · 2010
Earlier work this paper cites.
Spectral analysis of large dimensional random matrices
Zhidong Bai and Jack W Silverstein · 2010
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