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Sparse classification with paired covariates

Author

Listed:
  • Armin Rauschenberger

    (Amsterdam UMC, VU University Amsterdam
    University of Luxembourg)

  • Iuliana Ciocănea-Teodorescu

    (Amsterdam UMC, VU University Amsterdam)

  • Marianne A. Jonker

    (Radboud University Medical Center)

  • Renée X. Menezes

    (Amsterdam UMC, VU University Amsterdam)

  • Mark A. Wiel

    (Amsterdam UMC, VU University Amsterdam
    University of Cambridge)

Abstract

This paper introduces the paired lasso: a generalisation of the lasso for paired covariate settings. Our aim is to predict a single response from two high-dimensional covariate sets. We assume a one-to-one correspondence between the covariate sets, with each covariate in one set forming a pair with a covariate in the other set. Paired covariates arise, for example, when two transformations of the same data are available. It is often unknown which of the two covariate sets leads to better predictions, or whether the two covariate sets complement each other. The paired lasso addresses this problem by weighting the covariates to improve the selection from the covariate sets and the covariate pairs. It thereby combines information from both covariate sets and accounts for the paired structure. We tested the paired lasso on more than 2000 classification problems with experimental genomics data, and found that for estimating sparse but predictive models, the paired lasso outperforms the standard and the adaptive lasso. The R package palasso is available from cran.

Suggested Citation

  • Armin Rauschenberger & Iuliana Ciocănea-Teodorescu & Marianne A. Jonker & Renée X. Menezes & Mark A. Wiel, 2020. "Sparse classification with paired covariates," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 571-588, September.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:3:d:10.1007_s11634-019-00375-6
    DOI: 10.1007/s11634-019-00375-6
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    References listed on IDEAS

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    1. Bárbara Andrade Barbosa & Saskia D. Asten & Ji Won Oh & Arantza Farina-Sarasqueta & Joanne Verheij & Frederike Dijk & Hanneke W. M. Laarhoven & Bauke Ylstra & Juan J. Garcia Vallejo & Mark A. Wiel & Y, 2021. "Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

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