  In machine learning, one must acquire labels to help supervise a model that
will be able to generalize to unseen data. However, the labeling process can be
tedious, long, costly, and error-prone. It is often the case that most of our
data is unlabeled. Semi-supervised learning (SSL) alleviates that by making
strong assumptions about the relation between the labels and the input data
distribution. This paradigm has been successful in practice, but most SSL
algorithms end up fully trusting the few available labels. In real life, both
humans and automated systems are prone to mistakes; it is essential that our
algorithms are able to work with labels that are both few and also unreliable.
Our work aims to perform an extensive empirical evaluation of existing
graph-based semi-supervised algorithms, like Gaussian Fields and Harmonic
Functions, Local and Global Consistency, Laplacian Eigenmaps, Graph
Transduction Through Alternating Minimization. To do that, we compare the
accuracy of classifiers while varying the amount of labeled data and label
noise for many different samples. Our results show that, if the dataset is
consistent with SSL assumptions, we are able to detect the noisiest instances,
although this gets harder when the number of available labels decreases. Also,
the Laplacian Eigenmaps algorithm performed better than label propagation when
the data came from high-dimensional clusters.
