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There has been increased interest in devising learning techniques thatcombine unlabeled data with labeled data ? i.e. semi-supervised learning.However, to the best of our knowledge, no study has been performed acrossvarious techniques and different types and amounts of labeled and unlabeleddata. Moreover, most of the published work on semi-supervised learningtechniques assumes that the labeled and unlabeled data come from the samedistribution. It is possible for the labeling process to be associated with aselection bias such that the distributions of data points in the labeled andunlabeled sets are different. Not correcting for such bias can result in biasedfunction approximation with potentially poor performance. In this paper, wepresent an empirical study of various semi-supervised learning techniques on avariety of datasets. We attempt to answer various questions such as the effectof independence or relevance amongst features, the effect of the size of thelabeled and unlabeled sets and the effect of noise. We also investigate theimpact of sample-selection bias on the semi-supervised learning techniquesunder study and implement a bivariate probit technique particularly designed tocorrect for such bias.

Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains