Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging

Abstract

We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown. Based on a model with a low-rank component plus a sparse perturbation, and an initial graph estimation, our proposed algorithm simultaneously learns the low-rank component and refines the graph. Our evaluations using synthetic and real brain imaging data in unsupervised and supervised classification tasks demonstrate encouraging performance.

arXiv commentAccepted by ICASSP 2017

References in corpus (5)

• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Graph Frequency Analysis of Brain Signals
• Fast Robust PCA on Graphs
• Learning Laplacian Matrix in Smooth Graph Signal Representations
• Robust Principal Component Analysis on Graphs

Cited by in corpus (2)

• A Graph Signal Processing View on Functional Brain Imaging
• Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction