Journal of Computational and Graphical Statistics· 2012Q1
A Sparse-Group Lasso
- 1,385citations
- Q1SCImago
- 2012year
Short summary
A new Sparse-Group Lasso model with L1 and L2 penalties achieves both group-wise and within-group sparsity in high-dimensional linear regression.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces the Sparse-Group Lasso model for high-dimensional linear regression.
- The model enforces sparsity at both the group and within-group levels using L1 and L2 penalties.
- An accelerated generalized gradient descent algorithm is proposed for efficient model fitting.
- The model and algorithm are extended to handle convex loss functions.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Journal of Computational and Graphical Statistics, 2012 · DOI ↗
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