Abstract
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features. We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present a few case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.
Original language | English |
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Pages (from-to) | 497-526 |
Number of pages | 30 |
Journal | Annals of Statistics |
Volume | 47 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2019 |
Bibliographical note
Publisher Copyright:© Institute of Mathematical Statistics, 2019
Keywords
- Kernel machines
- Recursive feature elimination
- Support vector machines
- Variable selection
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty