Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

On Sparsity Inducing Regularization Methods for Machine Learning

Abstract : 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 which the regularizer can be expressed as the composition of a convex function $\omega$ with a linear function. This setting includes several methods such the group Lasso, the Fused Lasso, multi-task learning and many more. We present a general approach for solving regularization problems of this kind, under the assumption that the proximity operator of the function $\omega$ is available. Furthermore, we comment on the application of this approach to support vector machines, a technique pioneered by the groundbreaking work of Vladimir Vapnik.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.inria.fr/hal-00855984
Contributor : Puneet Kumar Dokania Connect in order to contact the contributor
Submitted on : Friday, August 30, 2013 - 11:49:32 AM
Last modification on : Friday, January 21, 2022 - 3:01:24 AM

Links full text

Identifiers

  • HAL Id : hal-00855984, version 1
  • ARXIV : 1303.6086

Collections

Citation

Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil. On Sparsity Inducing Regularization Methods for Machine Learning. 2013. ⟨hal-00855984⟩

Share

Metrics

Record views

154