Add to ORKGWe 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)
Overparametrization often helps improve the generalization performance. This paper presents a dual v...
Abstract—The prospect of carrying out data mining on cheaply compressed versions of high dimensional...
78We consider the problem of predicting as well as the best linear combination of d given functions ...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
The parameters of the multiple linear regression are estimated using least squares ( B̂LS ) and unbi...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
Includes bibliographical references (pages 51-53)In the standard regression technique, ordinary leas...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relatio...
Overparametrization often helps improve the generalization performance. This paper presents a dual v...
Abstract—The prospect of carrying out data mining on cheaply compressed versions of high dimensional...
78We consider the problem of predicting as well as the best linear combination of d given functions ...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
The parameters of the multiple linear regression are estimated using least squares ( B̂LS ) and unbi...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
Includes bibliographical references (pages 51-53)In the standard regression technique, ordinary leas...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relatio...
Overparametrization often helps improve the generalization performance. This paper presents a dual v...
Abstract—The prospect of carrying out data mining on cheaply compressed versions of high dimensional...
78We consider the problem of predicting as well as the best linear combination of d given functions ...