Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning

TL;DR

This paper reviews recent mathematical and statistical advances in graph-based Bayesian semi-supervised learning, emphasizing label propagation techniques and their theoretical foundations.

Contribution

It provides a detailed overview of mathematical tools and ideas underlying the statistical accuracy and computational efficiency of graph-based Bayesian SSL.

Findings

Mathematical frameworks for label propagation

Analysis of statistical accuracy in SSL

Computational methods for Bayesian SSL

Abstract

In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data. However, despite the unprecedented ease with which data are now collected and stored, labeling data by supplementing each feature with an informative tag remains to be challenging. Illustrative tasks where the labeling process requires expert knowledge or is tedious and time-consuming include labeling X-rays with a diagnosis, protein sequences with a protein type, texts by their topic, tweets by their sentiment, or videos by their genre. In these and numerous other examples, only a few features may be manually labeled due to cost and time constraints. How can we best propagate label information from a small number of expensive labeled features to a vast number of unlabeled ones? This is the question addressed by semi-supervised learning (SSL). This article overviews recent foundational developments on graph-based Bayesian SSL, a probabilistic framework for label propagation using similarities between features. SSL is an active research area and a thorough review of the extant literature is beyond the scope of this article. Our focus will be on topics drawn from our own research that illustrate the wide range of mathematical tools and ideas that underlie the rigorous study of the statistical accuracy and computational efficiency of graph-based Bayesian SSL.