Feature elimination in kernel machines in moderately high dimensions

Sayan Dasgupta; Yair Goldberg; Michael R. Kosorok

doi:10.1214/18-AOS1696

Feature elimination in kernel machines in moderately high dimensions

Sayan Dasgupta, Yair Goldberg, Michael R. Kosorok

Department of Statistics

Research output: Contribution to journal › Article › peer-review

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
Pages (from-to)	497-526
Number of pages	30
Journal	Annals of Statistics
Volume	47
Issue number	1
DOIs	https://doi.org/10.1214/18-AOS1696
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

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10.1214/18-AOS1696

Cite this

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Feature elimination in kernel machines in moderately high dimensions

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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