3 papers
cs.IT2016
Convex block-sparse linear regression with expanders -- provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad +3
Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization…
cs.LG2013
On Sparsity Inducing Regularization Methods for Machine Learning
Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli +1
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in wh…
cs.LG2011
A General Framework for Structured Sparsity via Proximal Optimization
Andreas Argyriou, Luca Baldassarre, Jean Morales +1
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as par…