Sparsity and Smoothness Via the Fused Lasso
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Résumé
Summary The lasso penalizes a least squares regression by the sum of the absolute values (L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0). We propose the ‘fused lasso’, a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes the L1-norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also sparsity of their differences—i.e. local constancy of the coefficient profile. The fused lasso is especially useful when the number of features p is much greater than N, the sample size. The technique is also extended to the ‘hinge’ loss function that underlies the support vector classifier. We illustrate the methods on examples from protein mass spectroscopy and gene expression data.
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La notice
- Revue
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
- Thématique
- Statistical Methods and Inference
- Domaine
- Mathematics
- Établissements canadiens
- University of Toronto
- Organismes subventionnaires
- Office of Naval ResearchNational Institutes of HealthNational Science Foundation
- Mots-clés
- Lasso (programming language)Elastic net regularizationMathematicsSmoothnessNorm (philosophy)Applied mathematicsLeast-squares function approximationRegressionClassifier (UML)Linear regressionAlgorithmMathematical optimizationComputer sciencePattern recognition (psychology)Artificial intelligenceStatisticsMathematical analysis
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- oui