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)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 SAM - Statistique Apprentissage Machine
3 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
4 G-SCOP_OC - Optimisation Combinatoire
5 MESCAL - Middleware efficiently scalable

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.

Mots clés: Logistic regression; Model selection; Text categorization; Large scale categorization

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

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, 2011, 32 (2), pp.101-106. ⟨10.1016/j.patrec.2010.09.023⟩. ⟨hal-00633629⟩