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Efficient ℓ0 -norm feature selection based on augmented and penalized minimization.

Xiang Li1, Shanghong Xie2, Donglin Zeng3

  • 1Statistics and Decision Sciences, Janssen Research & Development, LLC, Raritan, NJ, USA.

Statistics in Medicine
|October 31, 2017
PubMed
Summary
This summary is machine-generated.

A new method, augmented penalized minimization-L0 (APM-L0), accurately identifies prognostic biomarkers using L0-norm penalized regression. This computationally efficient approach outperforms existing techniques in selection accuracy and speed for genomic and imaging data.

Keywords:
ADMMbiomarker signaturecensored datavariable selectionℓ0-penalty

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics
  • Medical Imaging

Background:

  • High-throughput genomics and imaging generate vast numbers of prognostic biomarkers.
  • Penalized regression is crucial for identifying biomarkers associated with disease outcomes.
  • Exact L0-norm minimization for variable selection is computationally intractable (NP-hard).

Purpose of the Study:

  • To develop a computationally tractable and efficient method for L0-norm penalized variable selection.
  • To introduce the augmented penalized minimization-L0 (APM-L0) procedure for biomarker discovery.
  • To improve upon existing methods in terms of selection accuracy and computational speed.

Main Methods:

  • Proposed a novel 2-stage procedure, APM-L0, for L0-norm penalized variable selection.
  • Iterative approach combining convex regularized regression and hard-thresholding estimation.
  • Utilized a 1-step coordinate descent algorithm for computational efficiency in the first stage.

Main Results:

  • APM-L0 closely targets the L0-norm while maintaining computational tractability.
  • Demonstrated superior performance in selection accuracy and computational speed via simulations and real data.
  • The method is available as an R-package (APML0).

Conclusions:

  • APM-L0 offers an effective and efficient solution for biomarker selection using L0-norm regularization.
  • The proposed method advances the field of penalized regression for high-dimensional data analysis.
  • APM-L0 provides a valuable tool for researchers in genomics, imaging, and disease outcome studies.