The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
A Sparse-Group Lasso
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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Metadata
- Authors
- Simon, N., Friedman, J., Hastie, T., & Tibshirani, R.
- Affiliations
- Stanford University (US)
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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