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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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Regression shrinkage and selection via the lasso
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Self-citationRegularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Self-citationRegularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Self-citationModel Selection and Estimation in Regression with Grouped Variables
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The Group Lasso for Logistic Regression
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NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
Becker, S., Bobin, J., & Candès, E. J. (2009) · Cited by 897
Gradient methods for minimizing composite objective function
Nesterov, Y. E. (2007) · Cited by 867
Group lasso with overlap and graph lasso
Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842
A note on the group lasso and a sparse group lassoPreprint
Friedman, J. H., Hastie, T., & Tibshirani, R. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.0736 · Cited by 645
Self-citationBurczynski, M. E., Peterson, R. L., Twine, N. C., Zuberek, K., Brodeur, B. J., Casciotti, L., Maganti, V., Reddy, P., Strahs, A. L., Immermann, F., et al. (2006) · Journal of Molecular Diagnostics · doi:10.2353/jmoldx.2006.050079 · Cited by 249
Association screening of common and rare genetic variants by penalized regression
Zhou, H., Sehl, M. E., Sinsheimer, J. S., & Lange, K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq448 · Cited by 137 · cited 1×
“This criterion was also discussed in the article by Zhou et al. (2010).”
BackgroundExact block-wise optimization in group lasso and sparse group lasso for linear regressionPreprint
Foygel, R., & Drton, M. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.3320 · Cited by 24
Research works printed but not linked: 1
- 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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The gut microbiome in atherosclerotic cardiovascular disease
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Group sparse regularization for deep neural networks
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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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