2016/09/26 by Rui Liu, Liu, Rui, Hossein Nejati +6 · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced Graph Neural Networks #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1609.08221
Accepted by ICASSP 2017
openalex publication_date 2016/09/26 · openalex created_date 2016/10/07 · arxiv created 2017/01/09 · arxiv updated 2018/03/07 · openalex updated_date 2026/07/28
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.