Feature elimination in kernel machines in moderately high dimensions

Sayan Dasgupta, Yair Goldberg, Michael R. Kosorok

Research output: Contribution to journalArticlepeer-review


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
Issue number1
StatePublished - Feb 2019

Bibliographical note

Publisher Copyright:
© 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


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