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Mathematical programming for simultaneous feature selection and outlier detection under l1 norm

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  • Barbato, Michele
  • Ceselli, Alberto

Abstract

The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers.

Suggested Citation

  • Barbato, Michele & Ceselli, Alberto, 2024. "Mathematical programming for simultaneous feature selection and outlier detection under l1 norm," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1070-1084.
    • Handle: RePEc:eee:ejores:v:316:y:2024:i:3:p:1070-1084
    DOI: 10.1016/j.ejor.2024.03.035
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    References listed on IDEAS

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