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WB-graphs: a within versus between group similarity interplay

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  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

A method for balancing the within/between group similarity is proposed in the frameworkof estimating graphs when data from multiple groups or classes are available. The method leverages connections between the Karush-Kuhn-Tucker (KKT) conditions for estimating ℓ1 penalized graphs in order to define an optimization problem that can be solved with already existing, known algorithms from the literature. The method is illustrated on an fMRI dataset and with a simulated, controlled experiment. Statistical guarantees are as well provided.

Suggested Citation

  • Pircalabelu, Eugen, 2022. "WB-graphs: a within versus between group similarity interplay," LIDAM Discussion Papers ISBA 2022007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2022007
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    References listed on IDEAS

    as
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    4. Pircalabelu, Eugen & Claeskens, Gerda, 2020. "Community-Based Group Graphical Lasso," LIDAM Reprints ISBA 2020006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
    6. Cai, Tony & Liu, Weidong & Luo, Xi, 2011. "A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 594-607.
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    More about this item

    Keywords

    Within/between group similarity ; Penalized graphical models ; Differential networks;
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