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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
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...
Anomalies in the Foundations of Ridge Regression
Constrained optimization incomplete use of LaGrange’s method nonsingular distributions alternative foundations
2010/4/27
Anomalies persist in the foundations of ridge regression as set forth in Hoerl
and Kennard (1970) and subsequently. Conventional ridge estimators and their properties
do not follow on constraining l...