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Sharper Bounds for Regularized Data Fitting *

unpublished

We study matrix sketching methods for regularized variants of linear regression, low rank approximation , and canonical correlation analysis. Our main focus is on sketching techniques which preserve the objective function value for regularized problems, which is an area that has remained largely unexplored. We study regularization both in a fairly broad setting, and in the specific context of the popular and widely used technique of ridge regularization; for the latter , as applied to each of

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