Bias-Variance Tradeoff of Graph Laplacian Regularizer

Abstract

This letter presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semisupervised learning tasks. The scaling law of the optimal regularization parameter is specified in terms of the spectral graph properties and a novel signal-to-noise ratio parameter, which suggests that selecting a mediocre regularization parameter is often suboptimal. The analysis is applied to three applications, including random, band-limited, and multiple-sampled graph signals. Experiments on synthetic and real-world graphs demonstrate near-optimal performance of the established analysis.