A graph-based classification method is proposed both for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex regularization term and a concave loss function with a trade-off parameter carefully chosen so that the objective function remains convex. As shown experimentally, the advantage of considering a concave loss function is that the learning problem becomes more robust in the presence of noisy labels. Furthermore, the loss function considered is then more similar to a classification loss while several other methods treat graph-based classification problems as regression problems.
['Carlos M. Alaíz', 'Michaël Fanuel', 'Johan A. K. Suykens']
Robust Classification of Graph-Based Data