Semi-supervised learning

TL;DR

This paper discusses semi-supervised learning, proposing a new algorithm that asymptotically matches the best theoretical performance under certain conditions, highlighting limitations in semi-parametric classification.

Contribution

Introduces a new semi-supervised learning algorithm that achieves optimal asymptotic performance under specific assumptions.

Findings

The proposed algorithm attains asymptotic optimality with infinite unlabeled data.

Semi-parametric classification is effective only for well-conditioned problems.

The algorithm's success depends on certain necessary but reasonable assumptions.

Abstract

Semi-supervised learning deals with the problem of how, if possible, to take advantage of a huge amount of not classified data, to perform classification, in situations when, typically, the labelled data are few. Even though this is not always possible (it depends on how useful is to know the distribution of the unlabelled data in the inference of the labels), several algorithm have been proposed recently. A new algorithm is proposed, that under almost neccesary conditions, attains asymptotically the performance of the best theoretical rule, when the size of unlabeled data tends to infinity. The set of necessary assumptions, although reasonables, show that semi-parametric classification only works for very well conditioned problems.