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Abstract

Considerable progress was recently achieved on semi-supervised
learning, which differs from the traditional supervised learning by
additionally exploring the information of the unlabelled examples.
However, a disadvantage of many existing methods is that it does
not generalize to unseen inputs. This paper investigates learning
methods that effectively make use of both labelled and unlabelled
data to build predictive functions, which are defined on not just
the seen inputs but the whole space. As a nice property, the proposed
method allows effcient training and can easily handle new
test points. We validate the method based on both toy data and
real world data sets.