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A Risk Comparison of Ordinary Least Squares vs Ridge Regression

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arxiv 1105.0875 v2 pith:DNPESTJ7 submitted 2011-05-04 stat.ML

Paramveer S. Dhillon , Dean P. Foster , Sham M. Kakade , Lyle H. Ungar This is my paper · ORCID
classification stat.ML
keywords leastordinaryregressionrisksquaresridgesubspaceanalysis
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We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method is within a constant factor (namely 4) of the risk of ridge regression.

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