| Paper ID | SPTM-12.5 |
| Paper Title |
ADAPTIVE SUBSAMPLING OF MULTIDOMAIN SIGNALS WITH PRODUCT GRAPHS |
| Authors |
Théo Gnassounou, Pierre Humbert, Laurent Oudre, Ecole Normale Superieure Paris Saclay, France |
| Session | SPTM-12: Sampling, Filtering and Denoising over Graphs |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose an adaptive subsampling method for multidomain signals based on the constrained learning of a product graph. Given an input multidomain signal, we search for a product graph on which the signal is bandlimited, i.e. have limited spectral occupancy. The subsampling procedure described in this article is composed of two successive steps. First, we use the input data to learn a graph that will be optimized to favor efficient sampling. Then, we derive an algorithm for choosing the best nodes and provide a sampling strategy for multidomain signals. Experiments on synthetic data and two real datasets show the efficiency of the proposed method and its relevance for multidomain data compression and storing. |
