Epigraphical proximal projection for sparse multiclass SVM - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Epigraphical proximal projection for sparse multiclass SVM

Abstract

Sparsity inducing penalizations are useful tools in variational methods for machine learning. In this paper, we design a learning algorithm for multiclass support vector machines that allows us to enforce sparsity through various nonsmooth reg-ularizations, such as the mixed L1,p-norm with p ≥ 1. The proposed constrained convex optimization approach involves an epigraphical constraint for which we derive the closed-form expression of the associated projection. This sparse multiclass SVM problem can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments carried out for handwritten digits demonstrate the interest of considering nonsmooth sparsity-inducing reg-ularizations and the efficiency of the proposed epigraphical projection method.
Fichier principal
Vignette du fichier
main_rev1.pdf (370.08 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01796717 , version 1 (21-05-2018)

Identifiers

Cite

Giovanni Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet, Beatrice Pesquet-Popescu. Epigraphical proximal projection for sparse multiclass SVM. IEEE International Conference on Acoustics, Speech and Signal Processing, May 2014, Florence, Italy. ⟨10.1109/ICASSP.2014.6855222⟩. ⟨hal-01796717⟩
173 View
123 Download

Altmetric

Share

Gmail Facebook X LinkedIn More