Improved Graph-based semi-supervised learning Schemes

TL;DR

This paper enhances graph-based semi-supervised learning algorithms to improve classification accuracy on large, imbalanced datasets with limited labels, demonstrating superior performance through experimental validation.

Contribution

It introduces novel modifications to Gaussian Random Fields and Poisson Learning algorithms, resulting in more accurate and robust semi-supervised learning methods.

Findings

Improved accuracy over traditional methods

Enhanced robustness on imbalanced datasets

Demonstrated efficiency through experiments

Abstract

In this work, we improve the accuracy of several known algorithms to address the classification of large datasets when few labels are available. Our framework lies in the realm of graph-based semi-supervised learning. With novel modifications on Gaussian Random Fields Learning and Poisson Learning algorithms, we increase the accuracy and create more robust algorithms. Experimental results demonstrate the efficiency and superiority of the proposed methods over conventional graph-based semi-supervised techniques, especially in the context of imbalanced datasets.