Name
Title | ||
|---|---|---|
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. The refined graph improves the effectiveness of the graph smoothness constraint and increases the accuracy of the low-rank estimation. We derive the learning steps using ADMM. Our evaluations using synthetic and real brain imaging data in a supervised classification task demonstrate encouraging performance. |
Year | Venue | DocType |
|---|---|---|
2017 | ICASSP | Conference |
Volume | Citations | PageRank |
abs/1609.08221 | 2 | 0.41 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Liu | 1 | 2 | 1.76 |
| Hossein Nejati | 2 | 32 | 5.29 |
| Ngai-Man Cheung | 3 | 750 | 67.36 |