Simultaneous Low-rank Component and Graph Estimation for   High-dimensional Graph Signals: Application to Brain Imaging
  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. 
Rui Liu, Hossein Nejati, Seyed Hamid Safavi, Ngai-Man Cheung
cs.CV cs.LG