Algorithms for Semisupervised learning on graphs

Abstract

Laplacian regularization has been extensively used in a wide variety of semi-supervised learning tasks over the past fifteen years. In recent years, limitations of the Laplacian regularization have been exposed, leading to the development of a general class of Lp-based Laplacian regularization models. We propose novel algorithms to solve the resulting optimization problem, as the amount of unlabeled data increases to infinity, while the amount of labeled data remains fixed and is very small. We explore a practical application to recommender systems.