A Risk Comparison of Ordinary Least Squares vs Ridge Regression

05/04/2011

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by   Paramveer S. Dhillon, et al.

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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.