Proximal methods for the latent group lasso penalty
We consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual ℓ[subscript 1] and the group lasso penalty, by allowing the subsets to overlap. Such regularizations lead to nonsmooth problems that are difficult to optimize, and we pr...
Main Authors: | Villa, Silvia, Rosasco, Lorenzo Andrea, Mosci, Sofia, Verri, Alessandro |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
Format: | Article |
Language: | English |
Published: |
Springer US
2016
|
Online Access: | http://hdl.handle.net/1721.1/103284 https://orcid.org/0000-0001-6376-4786 |
Similar Items
-
Iterative Projection Methods for Structured Sparsity Regularization
by: Rosasco, Lorenzo, et al.
Published: (2009) -
Nonparametric Sparsity and Regularization
by: Rosasco, Lorenzo Andrea, et al.
Published: (2013) -
Nonparametric Sparsity and Regularization
by: Mosci, Sofia, et al.
Published: (2011) -
Convergence of Stochastic Proximal Gradient Algorithm
by: Rosasco, Lorenzo, et al.
Published: (2021) -
The l[subscript 1]-l[subscript 2] regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines
by: Varesio, Luigi, et al.
Published: (2010)