| Paper ID | MLSP-30.4 |
| Paper Title |
Mask Combination of Multi-Layer Graphs for Global Structure Inference |
| Authors |
Eda Bayram, Dorina Thanou, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Elif Vural, Middle East Technical University, Turkey; Pascal Frossard, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
| Session | MLSP-30: Graph Signal Processing |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-GKM] Graphical and kernel methods |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Structure inference is an important task for network data processing and analysis. Many approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a data space whose inner relationships are encoded by multi-layer graphs. We aim at properly exploiting the information originating from each layer to infer the global structure underlying the signals. We thus present a novel method for combining the multiple graphs into a global graph using mask matrices, which are estimated through an optimization problem that accommodates the multi-layer graph information and a signal representation model. The proposed mask combination method also estimates the contribution of each graph layer in the structure of signals. The experiments conducted both on synthetic and real-world data suggest that integrating the multi-layer graph representation of the data in the structure inference framework enhances the learning procedure considerably by adapting to the quality and the quantity of the input data. |
