Title: On semi-supervised learning,
Abstract: Semi-supervised learning deals with the problem of how, if possible, to take
advantage of a huge amount of unclassified data, to perform a classification in
situations when, typically, there is little labeled data. Even though this is
not always possible (it depends on how useful, for inferring the labels, it
would be to know the distribution of the unlabeled data), several algorithm
have been proposed recently. %but in general they are not proved to outperform
A new algorithm is proposed, that under almost necessary conditions, %and it is
proved that it attains asymptotically the performance of the best theoretical
rule as the amount of unlabeled data tends to infinity. The set of necessary
assumptions, although reasonable, show that semi-supervised classification only
works for very well conditioned problems. The focus is on understanding when
and why semi-supervised learning works when the size of the initial training
sample remains fixed and the asymptotic is on the size of the unlabeled data.
The performance of the algorithm is assessed in the well known "Isolet"
real-data of phonemes, where a strong dependence on the choice of the initial
training sample is shown.