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.
|Number of pages||30|
|Journal||Annals of Statistics|
|State||Published - Feb 2019|
Bibliographical noteFunding Information:
Received December 2015; revised November 2017. 1Supported in part by Israel Science Foundation Grant 1308/12 and supported in part by United States NCI Grant PO1 CA142538. MSC2010 subject classifications. 62G20. Key words and phrases. Kernel machines, support vector machines, variable selection, recursive feature elimination.
© Institute of Mathematical Statistics, 2019
- Kernel machines
- Recursive feature elimination
- Support vector machines
- Variable selection
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty