glasp-package | R Documentation |
Implements the the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse-Group Lasso regularization to calculate clusters as part of the model fit. Therefore, unlike Sparse-Group Lasso, our idea does not require prior specification of clusters between variables. To determine the clusters, we solve a particular case of sparse Singular Value Decomposition, with a regularization term that follows naturally from the Group Lasso penalty. Moreover, this paper proposes a unified implementation to deal with, but not limited to, linear regression, logistic regression, and proportional hazards models with right-censoring.
Maintainer: Juan C. Laria juank.laria@gmail.com (ORCID)
Other contributors:
Rosa E. Lillo [thesis advisor]
M. Carmen Aquilera-Morillo [thesis advisor]
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