Generalization error bounds in semi-supervised classification under the cluster assumption

Philippe Rigollet (1)

1 School of Mathematics - Georgia Institute of Technology

We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumption relating the behavior of the regression function to that of the marginal distribution. Seeger (2000) proposed the well-known "cluster assumption" as a reasonable one. We propose a mathematical formulation of this assumption and a method based on density level sets estimation that takes advantage of it to achieve fast rates of convergence both in the number of unlabeled examples and the number of labeled examples.

Mots clés: Semi-supervised learning; statistical learning theory; classification; cluster assumption; generalization bounds

Domaines: Statistiques [math.ST]; Apprentissage [cs.LG]

Philippe Rigollet. Generalization error bounds in semi-supervised classification under the cluster assumption. Journal of Machine Learning Research, 2007, 8 (Jul), pp.1369--1392. ⟨hal-00022528v4⟩