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Look-Ahead Screenin...
Look-Ahead Screening Rules for the Lasso
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- Larsson, Johan (author)
- Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM
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Makridis, Andreas (editor)
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Milienos, Fotios S. (editor)
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Papastamoulis, Panagiotis (editor)
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Parpoula, Christina (editor)
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Rakitzis, Athanasios (editor)
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(creator_code:org_t)
- 2021
- 2021
- English 5 s.
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In: 22nd European young statisticians meeting - proceedings. - 9789607943231 ; , s. 61-65
- Related links:
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https://portal.resea... (primary) (free)
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Abstract
Subject headings
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- The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to the number of observations. Solving the lasso in this high-dimensional setting can, however, be computationally demanding. Fortunately, this demand can be alleviated via the use of screening rules that discard predictors prior to fitting the model, leading to a reduced problem to be solved. In this paper, we present a new screening strategy: look-ahead screening. Our method uses safe screening rules to find a range of penalty values for which a given predictor cannot enter the model, thereby screening predictors along the remainder of the path. In experiments we show that these look-ahead screening rules outperform the active warm-start version of the Gap Safe rules.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- lasso
- screening rules
- safe screening rules
Publication and Content Type
- kon (subject category)
- ref (subject category)
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