The asymptotics of semi-supervised learning in discriminative probabilistic models

Authors:
Nataliya Sokolovska;Olivier Cappé;François Yvon
Affiliations:
LTCI, TELECOM ParisTech and CNRS, Paris, France;LTCI, TELECOM ParisTech and CNRS, Paris, France;Université Paris-Sud and LIMSI-CNRS, Orsay, France
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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Abstract

Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a challenging task. In this contribution, we introduce an original methodology for using unlabeled data through the design of a simple semi-supervised objective function. We prove that the corresponding semi-supervised estimator is asymptotically optimal. The practical consequences of this result are discussed for the case of the logistic regression model.