Random primal-dual proximal iterations for sparse multiclass SVM

Abstract : Sparsity-inducing penalties are useful tools in variational methods for machine learning. In this paper, we propose two block-coordinate descent strategies for learning a sparse multiclass support vector machine. The first one works by selecting a subset of features to be updated at each iteration, while the second one performs the selection among the training samples. These algorithms can be efficiently implemented thanks to the flexibility offered by recent randomized primal-dual proximal methods. Experiments carried out for the supervised classification of handwritten digits demonstrate the interest of considering the primal-dual approach in the context of block-coordinate descent, and the efficiency of the proposed algorithms is assessed through a comparison of execution times and classification errors. Index Terms— Sparsity, multiclass SVM, proximal algorithm, random update, block-coordinate descent.
Type de document :
Communication dans un congrès
IEEE International Workshop on Machine Learning for Signal Processing, Sep 2016, Vietri sul Mare, Italy. 2016, <10.1109/MLSP.2016.7738833>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01419697
Contributeur : Giovanni Chierchia <>
Soumis le : lundi 19 décembre 2016 - 18:14:20
Dernière modification le : samedi 18 février 2017 - 01:14:50

Identifiants

Citation

Giovanni Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet. Random primal-dual proximal iterations for sparse multiclass SVM. IEEE International Workshop on Machine Learning for Signal Processing, Sep 2016, Vietri sul Mare, Italy. 2016, <10.1109/MLSP.2016.7738833>. <hal-01419697>

Partager

Métriques

Consultations de la notice

257