Cited by 1,380Statistical Methods and InferenceSparse and Compressive Sensing TechniquesAdvanced Statistical Methods and Models

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A Sparse-Group Lasso

Simon, Noah, Friedman, Jerome, Hastie, Trevor, Tibshirani, Robert·2013·Journal of Computational and Graphical Statistics

DOI 10.1080/10618600.2012.681250

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Abstract

For high-dimensional supervised learning problems, often using problem-specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effects both on a group and within group level, we introduce a regularized model for linear regression with ℓ1 and ℓ2 penalties. We discuss the sparsity and other regularization properties of the optimal fit for this model, and show that it has the desired effect of group-wise and within group sparsity. We propose an algorithm to fit the model via accelerated generalized gradient descent, and extend this model and algorithm to convex loss functions. We also demonstrate the efficacy of our model and the efficiency of our algorithm on simulated data. This article has online supplementary material.

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Puig, A., Wiesel, A., and Hero, A., 2009. "“A Multidimensional Shrinkage-Thresholding Operator, Statistical Signal Processing,”". In: SSP'09, IEEE/SP 15th Workshop on Statistical Signal Processing. 2009. pp. 113–116.

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Authors: Simon, N., Friedman, J., Hastie, T., & Tibshirani, R.
Affiliations: Stanford University (US)
ORCID: Simon, Noah
Friedman, Jerome
Hastie, Trevor
Tibshirani, Robert
Venue: Journal of Computational and Graphical Statistics
Publisher: Informa UK Limited
Published: 2013-01
Volume: 22
Issue: 2
Pages: 231-245
ISSN: 1061-8600 (ISSN-L)
1537-2715
DOI: 10.1080/10618600.2012.681250
OpenAlex: W1987371344
Cited by: 1,380
References: 15
Citations by year: 2026: 82 · 2025: 94 · 2024: 125 · 2023: 98 · 2022: 120 · 2021: 152 · 2020: 132 · 2019: 135 · 2018: 93 · 2017: 116 · 2016: 84 · 2015: 71 · 2014: 52 · 2013: 22
FWCI: 44.86
Type: journal article
Language: en
Open access: closed
Locations: Journal of Computational and Graphical Statistics (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: sparse group lasso, high-dimensional linear regression, convex loss functions, regularization

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A Sparse-Group Lasso

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