Active Semi-Supervised Learning for Diffusions on Graphs

Abstract

In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal training set keeping the classifier in mind. Random or heuristic-driven selection does not care for the classification process or are trivially defined. We are interested in the graph structure formed by the data, as seen in citation, social and biological networks. Accordingly, active semi-supervised learning on graphs labels nodes to enhance the performance of classification. We propose a new methodology to perform active learning for diffusion-based semi-supervised classifiers. In particular, we focus on a classifier which diffuses probability distribu