Feature elimination in kernel machines in moderately high dimensions

Sayan Dasgupta, Yair Goldberg, Michael R. Kosorok

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)497-526
Number of pages30
JournalAnnals of Statistics
Volume47
Issue number1
DOIs
StatePublished - Feb 2019

Bibliographical note

Funding 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.

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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