A sparse version of the ridge logistic regression for large-scale text categorization

Sujeevan Aseervatham 1 Anestis Antoniadis 2 Éric Gaussier 3, * Michel Burlet 4 Yves Denneulin 5
* Auteur correspondant
1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 SAM - Statistique Apprentissage Machine
LJK - Laboratoire Jean Kuntzmann
3 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
4 G-SCOP_OC - OC
G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production
5 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : The ridge logistic regression has successfully been used in text categorization problems and it has been shown to reach the same performance as the Support Vector Machine but with the main advantage of computing a probability value rather than a score. However, the dense solution of the ridge makes its use unpractical for large scale categorization. On the other side, LASSO regularization is able to produce sparse solutions but its performance is dominated by the ridge when the number of features is larger than the number of observations and/or when the features are highly correlated. In this paper, we propose a new model selection method which tries to approach the ridge solution by a sparse solution. The method first computes the ridge solution and then performs feature selection. The experimental evaluations show that our method gives a solution which is a good trade-off between the ridge and LASSO solutions.
keyword : Large scale categorization Text categorization Logistic regression Model selection
Type de document :
Article dans une revue
Pattern Recognition Letters, Elsevier, 2011, 32 (2), pp.101-106. 〈10.1016/j.patrec.2010.09.023〉
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger
https://hal.archives-ouvertes.fr/hal-00633629
Contributeur : Marie Josèphe Perruet <>
Soumis le : lundi 15 octobre 2012 - 18:21:33
Dernière modification le : jeudi 7 février 2019 - 17:48:16
Document(s) archivé(s) le : mercredi 16 janvier 2013 - 02:35:10

Fichier

Aseervatham-PatternRecognition...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | G-SCOP_OSP | G-SCOP | G-SCOP_OSP_OC | LIG_TDCGE_AMA | LIG_TDCGE | LIG_SRCPR_MESCAL | LIG | LIG_SRCPR | LJK_PS | LJK_PS_SAM | LJK | LIPN | UNIV-PARIS13 | UGA | USPC | GALILE

Citation

Sujeevan Aseervatham, Anestis Antoniadis, Éric Gaussier, Michel Burlet, Yves Denneulin. A sparse version of the ridge logistic regression for large-scale text categorization. Pattern Recognition Letters, Elsevier, 2011, 32 (2), pp.101-106. 〈10.1016/j.patrec.2010.09.023〉. 〈hal-00633629〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

937

Téléchargements de fichiers

342

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD