Dhillon, Paramveer S.Foster, Dean PKakade, Sham MUngar, Lyle2023-05-232023-05-232013-06-012016-08-19https://repository.upenn.edu/handle/20.500.14332/47488We 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).risk inflationridge regressionpcaComputer SciencesArticle