Support recovery with Projected Stochastic Gates: Theory and application for linear models

Soham Jana, Henry Li, Yutaro Yamada, Ofir Lindenbaum

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise. We study the problem of estimating the model coefficients based on a recently proposed non-convex regularizer, namely the stochastic gates (STG) (Yamada et al., 2020). We suggest a new projection-based algorithm for solving the STG regularized minimization problem, and prove convergence and support recovery guarantees of the STG-estimator for a range of random and non-random design matrix setups. Our new algorithm has been shown to outperform the existing STG algorithm and other classical estimators for support recovery in various real and synthetic data analyses.

Original languageEnglish
Article number109193
JournalSignal Processing
Volume213
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Compressed sensing
  • Consistency
  • Feature selection
  • LASSO
  • Non-convex penalty
  • Orthogonal matching pursuit
  • SCAD
  • Sparsity
  • Stochastic gates
  • Support recovery

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