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)
Abstract—The prospect of carrying out data mining on cheaply compressed versions of high dimensional...
International audienceWe consider the problem of learning, from K data, a regression function in a l...
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 note, we provide an elementary analysis of the prediction error of ridge regression with ran...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
The parameters of the multiple linear regression are estimated using least squares ( B̂LS ) and unbi...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
78We consider the problem of predicting as well as the best linear combination of d given functions ...
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...
International audienceWe consider the problem of learning, from K data, a regression function in a l...
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 note, we provide an elementary analysis of the prediction error of ridge regression with ran...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
29 pagesInternational audienceWe consider the problem of robustly predicting as well as the best lin...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
The parameters of the multiple linear regression are estimated using least squares ( B̂LS ) and unbi...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
78We consider the problem of predicting as well as the best linear combination of d given functions ...
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...
International audienceWe consider the problem of learning, from K data, a regression function in a l...
78We consider the problem of predicting as well as the best linear combination of d given functions ...