p-voltages: Laplacian Regularization for Semi-Supervised Learning on High-Dimensional Data
Nick Bridle, Xiaojin Zhu · 2013
We investigate the p-voltages algorithm, which labels nodes in a graph based on their theoretical voltages in a reformulated system of electricity. Building on previous work concerning p-electric networks, we prove that the p-voltage solution is well-formed and has desirable properties for semisupervised learning. Our experiments confirm that the p-voltages algorithm does not suffer from the same weaknesses as the Laplacian Regularization algorithm (equivalent to p = 2) and therefore improves classification performance. However, our p-voltages algorithm does not outperform the state-of-the-art iterated Laplacian algorithm. 1.