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Abstract

We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled,
and the remaining points are unlabeled. The goal is to predict the
labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector
Machines (SVMs). The problem of our interest is if we can
implement a classifier which uses the unlabeled data information
in some way and has higher accuracy than the classifiers which use
the labeled data only. Recently we proposed a simple algorithm,
which can substantially benefit from large amounts of unlabeled
data and demonstrates clear superiority to supervised learning
methods. In this paper we further investigate the algorithm using
random walks and spectral graph theory, which shed light on the
key steps in this algorithm.