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.
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Pré-publication, Document de travail
12 pages. arXiv admin note: text overlap with arXiv:1104.1436. 2013
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https://hal.inria.fr/hal-00855984
Contributeur : Puneet Kumar Dokania <>
Soumis le : vendredi 30 août 2013 - 11:49:32
Dernière modification le : jeudi 29 mars 2018 - 13:36:02

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

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Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil. On Sparsity Inducing Regularization Methods for Machine Learning. 12 pages. arXiv admin note: text overlap with arXiv:1104.1436. 2013. 〈hal-00855984〉

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