Learning with local and global consistency
Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference.
A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution.
Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.
Research Areas
