Quantile-based fitting for graph signals

Abstract

We propose a quantile based fitting method for analyzing graph signals. Unlike traditional approaches for data fitting such as smoothing splines and quantile smoothing splines working on Euclidean space, the proposed method is designed for graph domain, considering the inherent graph structure. In contrast to prevalent graph signal denoising methods that rely on optimization problem with $L_2$-norm fidelity, our approach provides denoised signals that are robust to the existence of outliers, and identifies varying structural relationships within graph signals. We validate the efficacy of our method through comprehensive simulation studies and real data analysis.

Contribution

We proposed a quantile based fitting method for analyzing graph signals.

Code

To be uploaded.