Abstract
In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional network control theory and signal processing on networks. In addition, it provides theoretical guarantees for the recovery of the topological structure of a deterministic linear dynamical system from input-output observations even though the input and state evolution networks can differ.
| Original language | English |
|---|---|
| Title of host publication | 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings |
| Place of Publication | Amsterdam (Netherlands) |
| Publisher | Eurasip |
| Pages | 1055-1059 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-9-0827-9705-3 |
| DOIs | |
| Publication status | Published - 1 Aug 2020 |
| Event | EUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands Duration: 18 Jan 2021 → 22 Jan 2021 Conference number: 28th |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| Volume | 2021-January |
| ISSN (Print) | 2219-5491 |
Conference
| Conference | EUSIPCO 2020 |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 18/01/21 → 22/01/21 |
| Other | Date change due to COVID-19 (former date August 24-28 2020) |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Graph signal processing
- Signal processing over networks
- State-space models
- Topology identification