A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar; 14(46):1505−1511, 2013.
Abstract
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 (PCA-OLS) is within a constant factor (namely 4) of the risk of ridge regression (RR).
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