Feature Selection for Ridge Regression with Provable Guarantees
Feature Selection for Ridge Regression with Provable Guarantees
We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained …