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A study on tuning parameter selection for the high-dimensional lasso
We compare our risk estimators to existing methods with an extensive simulation and derive some theoretical justification.
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets
PCA is a classical dimension reduction method that involves the projection of the data onto the subspace spanned by the leading eigenvectors of the covariance matrix.
Risk-consistency of cross-validation with lasso-type procedures
In practice, however, this oracle tuning parameter is inaccessible so one must use the data to select one.
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