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Generalized Linear Model Regression under Distance-to-set Penalties

NeurIPS 2017 · Learning theory and algorithms › Matrix and tensor methods · approach: High-dimensional estimation theory · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use distance penalties with MM instead of shrinkage penaltiesSparse Poisson and logistic regression · Synthetic sparse regressionmean squared errorHelped
Use distance penalties with MM instead of LASSOSparse Poisson and logistic regression · Synthetic sparse regressionrelative errorHelped
Use rank-set projections instead of spectral shrinkageMatrix regression · Synthetic cross-shaped rank-2 signalsignal recoveryHelped
Use distance-penalized MMEEG alcoholism regression · EEG alcoholismmisclassification error: 0.1475 vs 0.139Mixed
Use distance-penalized MM rather than relaxed LASSOGaussian linear regression · Gaussian regression simulationMSENo clear effect
Use distance-penalized MM rather than relaxed LASSOGaussian linear regression · Gaussian regression simulationaverage total runtime: 96.8 vs 4876.6 secondsHelped
Account for matrix structure with a rank constraint rather than sparsity alone (ablation)Distance-penalized matrix regression · Synthetic cross-shaped rank-2 signalsignal recoveryHelped
Set the rank constraint to 2 (ablation)Distance-penalized matrix regression · Synthetic cross-shaped rank-2 signalsignal recoveryHelped
Vary the rank constraint from 1 to 8 (ablation)Distance-penalized matrix regression · Synthetic cross-shaped rank-2 signalsignal recoveryNo clear effect
Use an ℓ1 penalty instead of a rank constraint (ablation)EEG alcoholism regression · EEG alcoholismuseful signal recoveryHurt
Use nuclear-norm shrinkage instead of rank-set distance penalization (ablation)EEG alcoholism regression · EEG alcoholismlow-rank solutionHurt
Use rank restriction rather than sparsity restriction (ablation)Matrix regression · Synthetic cross-shaped rank-2 signalsignal recoveryHelped

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