Name
Abstract | ||
|---|---|---|
In many applications signals reside on the vertices of weighted graphs. Thus, there is the need to learn low dimensional representations for graph signals that will allow for data analysis and interpretation. Existing unsupervised dimensionality reduction methods for graph signals have focused on dictionary learning. In these works the graph is taken into consideration by imposing a structure or a parametrization on the dictionary and the signals are represented as linear combinations of the atoms in the dictionary. However, the assumption that graph signals can be represented using linear combinations of atoms is not always appropriate. In this paper we propose a novel representation framework based on non-linear and geometry-aware combinations of graph signals by leveraging the mathematical theory of Optimal Transport. We represent graph signals as Wasserstein barycenters and demonstrate through our experiments the potential of our proposed framework for low-dimensional graph signal representation. |
Year | Venue | Keywords |
|---|---|---|
2019 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | graph signal processing, dimensionality reduction, optimal transport, barycenters |
Field | DocType | ISSN |
Graph,Linear combination,Dictionary learning,Dimensionality reduction,Vertex (geometry),Pattern recognition,Parametrization,Computer science,Mathematical theory,Algorithm,Graph signal processing,Artificial intelligence | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Effrosyni Simou | 1 | 0 | 0.68 |
| Pascal Frossard | 2 | 19 | 3.50 |